Main network model diagnostics - unbalanced statistics
This file shows diagnostics for main network models fit using unbalanced racial/ethnic mixing matrices and degree terms as reported by egos. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
load(file = "/homes/dpwhite/R/GitHub Repos/WHAMP/Model fits and simulations/Fit tests and debugging/est/fit.m.buildup.unbal.rda")Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|---|
| edges | 2240.5 | 2240.5 | 2240.5 | 2240.5 | 2240.5 | 2240.5 | 2240.5 | 2240.5 |
| nodefactor.deg.pers.1 | NA | NA | NA | 474.0 | 474.0 | 474.0 | 474.0 | 474.0 |
| nodefactor.deg.pers.2 | NA | NA | NA | 605.0 | 605.0 | 605.0 | 605.0 | 605.0 |
| nodefactor.race..wa.B | NA | 208.0 | 208.0 | 208.0 | 208.0 | 208.0 | 208.0 | 208.0 |
| nodefactor.race..wa.H | NA | 535.0 | 535.0 | 535.0 | 535.0 | 535.0 | 535.0 | 535.0 |
| nodefactor.region.EW | NA | NA | NA | NA | 445.6 | 445.6 | 445.6 | 445.6 |
| nodefactor.region.OW | NA | NA | NA | NA | 1278.1 | 1278.1 | 1278.1 | 1278.1 |
| nodematch.race..wa.B | NA | NA | 31.2 | 31.2 | 31.2 | 31.2 | 31.2 | 31.2 |
| nodematch.race..wa.H | NA | NA | 123.3 | 123.3 | 123.3 | 123.3 | 123.3 | 123.3 |
| nodematch.race..wa.O | NA | NA | 1638.9 | 1638.9 | 1638.9 | 1638.9 | 1638.9 | 1638.9 |
| absdiff.sqrt.age | NA | NA | NA | NA | NA | 1206.3 | 1206.3 | 1206.3 |
| degrange | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| mix.region.EW.KC | NA | NA | NA | NA | NA | NA | -Inf | NA |
| mix.region.EW.OW | NA | NA | NA | NA | NA | NA | -Inf | NA |
| mix.region.KC.OW | NA | NA | NA | NA | NA | NA | -Inf | NA |
| nodematch.region | NA | NA | NA | NA | NA | NA | NA | 2016.5 |
The control settings for these models are:
set.control.ergm = control.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## 0.2584 29.1738 0.1684 0.1687
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## -57.5 -19.5 0.5 19.5 57.5
##
##
## Sample statistics cross-correlations:
## edges
## edges 1
##
## Sample statistics auto-correlation:
## Chain 1
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.021311759
## Lag 2e+05 0.006913747
## Lag 3e+05 -0.015821787
## Lag 4e+05 0.003054700
## Lag 5e+05 0.001659161
## Chain 2
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.038893096
## Lag 2e+05 0.008780638
## Lag 3e+05 -0.002287174
## Lag 4e+05 -0.020430043
## Lag 5e+05 -0.015529059
## Chain 3
## edges
## Lag 0 1.0000000000
## Lag 1e+05 -0.0177714670
## Lag 2e+05 -0.0003294755
## Lag 3e+05 -0.0266891793
## Lag 4e+05 0.0088755247
## Lag 5e+05 0.0096310578
## Chain 4
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.012800823
## Lag 2e+05 0.007450237
## Lag 3e+05 -0.035238088
## Lag 4e+05 0.002876751
## Lag 5e+05 0.015557350
## Chain 5
## edges
## Lag 0 1.0000000000
## Lag 1e+05 -0.0054548704
## Lag 2e+05 -0.0009120409
## Lag 3e+05 0.0006025041
## Lag 4e+05 -0.0079666790
## Lag 5e+05 0.0025264678
## Chain 6
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.027293751
## Lag 2e+05 0.003653601
## Lag 3e+05 -0.006770046
## Lag 4e+05 -0.003018379
## Lag 5e+05 0.007868121
## Chain 7
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.015172629
## Lag 2e+05 -0.002182382
## Lag 3e+05 -0.006919308
## Lag 4e+05 0.006071705
## Lag 5e+05 0.009893214
## Chain 8
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.001121886
## Lag 2e+05 -0.010930717
## Lag 3e+05 0.002647498
## Lag 4e+05 -0.028508689
## Lag 5e+05 0.011023553
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -1.774
##
## Individual P-values (lower = worse):
## edges
## 0.0761076
## Joint P-value (lower = worse): 0.110726 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.463
##
## Individual P-values (lower = worse):
## edges
## 0.14347
## Joint P-value (lower = worse): 0.1416173 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.6308
##
## Individual P-values (lower = worse):
## edges
## 0.528151
## Joint P-value (lower = worse): 0.5287019 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.6044
##
## Individual P-values (lower = worse):
## edges
## 0.545568
## Joint P-value (lower = worse): 0.5381292 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.6822
##
## Individual P-values (lower = worse):
## edges
## 0.4951103
## Joint P-value (lower = worse): 0.5236003 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.5754
##
## Individual P-values (lower = worse):
## edges
## 0.5650061
## Joint P-value (lower = worse): 0.5345761 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.768
##
## Individual P-values (lower = worse):
## edges
## 0.4425155
## Joint P-value (lower = worse): 0.4456171 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.4798
##
## Individual P-values (lower = worse):
## edges
## 0.6313917
## Joint P-value (lower = worse): 0.6776458 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.5446 29.09 0.16795 0.16519
## nodefactor.race..wa.B 0.1272 11.79 0.06808 0.06726
## nodefactor.race..wa.H 0.1142 16.79 0.09697 0.09625
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -57.50 -20.500 -0.5000 19.500 56.50
## nodefactor.race..wa.B -23.00 -7.997 0.0032 8.003 23.00
## nodefactor.race..wa.H -32.98 -10.978 0.0220 11.022 33.02
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.27258005
## nodefactor.race..wa.B 0.2725801 1.00000000
## nodefactor.race..wa.H 0.3802482 0.02750431
## nodefactor.race..wa.H
## edges 0.38024824
## nodefactor.race..wa.B 0.02750431
## nodefactor.race..wa.H 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.0306955142 -0.045185354 0.0003787597
## Lag 2e+05 -0.0176717306 0.003181592 0.0008145705
## Lag 3e+05 -0.0059129372 -0.033190726 0.0149143693
## Lag 4e+05 -0.0185361432 0.012874995 -0.0005899606
## Lag 5e+05 -0.0006284204 -0.037491288 -0.0036692153
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.004735900 0.001718756 -0.0105949701
## Lag 2e+05 -0.016561893 0.003234964 -0.0071797685
## Lag 3e+05 0.021881418 0.007244600 0.0026880875
## Lag 4e+05 -0.005663435 -0.003831923 0.0001270214
## Lag 5e+05 0.029824205 0.006738128 -0.0105913886
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 -0.01058281 -0.017344375 0.020142400
## Lag 2e+05 0.01934298 -0.021331277 0.002824005
## Lag 3e+05 -0.03188353 -0.012253084 0.013549969
## Lag 4e+05 0.02926160 0.003937166 -0.004719217
## Lag 5e+05 -0.02612521 0.008052129 0.001919083
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008041777 -0.004149025 -0.021863917
## Lag 2e+05 -0.029401783 -0.025531110 -0.021383696
## Lag 3e+05 0.025536754 0.001237031 0.011432079
## Lag 4e+05 0.013367916 0.021066169 -0.019794832
## Lag 5e+05 -0.004986184 -0.005333816 -0.004319172
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.032412281 -0.014228743 -0.0295305625
## Lag 2e+05 0.016618921 -0.013868108 -0.0275281512
## Lag 3e+05 0.010090519 -0.002592426 0.0102127976
## Lag 4e+05 -0.042199905 -0.004605539 -0.0002331337
## Lag 5e+05 0.007116046 0.005642546 -0.0178356773
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.021080979 -0.016354272 -0.006400649
## Lag 2e+05 -0.013821603 0.011133737 -0.007072587
## Lag 3e+05 -0.009287061 0.027393191 -0.004836509
## Lag 4e+05 0.037394149 0.001649983 -0.002463360
## Lag 5e+05 -0.022328145 0.024217209 -0.029483405
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.016006268 -0.022793687 0.0030456370
## Lag 2e+05 0.003493309 -0.004603620 0.0082137286
## Lag 3e+05 0.023780886 -0.016736899 -0.0062477606
## Lag 4e+05 0.008830207 -0.008336089 0.0007973993
## Lag 5e+05 -0.010635984 -0.040810940 -0.0288721541
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.016623295 0.003165565 -0.032525364
## Lag 2e+05 0.005450470 0.012551951 0.028939148
## Lag 3e+05 -0.007705185 -0.003852331 0.007713364
## Lag 4e+05 0.015687234 -0.006296764 -0.014089011
## Lag 5e+05 0.037315564 -0.015040474 0.004781505
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.2095 -0.8668 -0.4193
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2264760 0.3860679 0.6749928
## Joint P-value (lower = worse): 0.6040104 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6987 -1.0471 0.0321
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4847340 0.2950420 0.9743886
## Joint P-value (lower = worse): 0.4848073 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2831 -0.1072 0.7440
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7771285 0.9146513 0.4568527
## Joint P-value (lower = worse): 0.9154585 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.0468 0.1007 1.9788
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.29520220 0.91978755 0.04783802
## Joint P-value (lower = worse): 0.2479544 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 2.53054 -0.05994 0.62401
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.01138869 0.95220164 0.53262340
## Joint P-value (lower = worse): 0.1302313 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.4098 -1.8064 0.2498
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.68198394 0.07085786 0.80271947
## Joint P-value (lower = worse): 0.3505584 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7146 0.4222 -1.1114
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4748388 0.6728580 0.2664013
## Joint P-value (lower = worse): 0.3675842 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.8075 -0.8192 0.8459
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4193924 0.4126684 0.3975982
## Joint P-value (lower = worse): 0.5046923 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -2.055 29.278 0.16904 0.17276
## nodefactor.race..wa.B 2.570 12.619 0.07286 0.07328
## nodefactor.race..wa.H 1.602 18.038 0.10414 0.11251
## nodematch.race..wa.B -2.093 4.907 0.02833 0.02962
## nodematch.race..wa.H -2.525 8.783 0.05071 0.06132
## nodematch.race..wa.O 2.282 26.643 0.15382 0.15383
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -59.50 -21.500 -2.500 17.5000 55.500
## nodefactor.race..wa.B -22.00 -5.997 2.003 11.0032 28.003
## nodefactor.race..wa.H -33.98 -10.978 2.022 14.0220 37.022
## nodematch.race..wa.B -11.18 -5.179 -2.179 0.8213 7.821
## nodematch.race..wa.H -19.31 -8.312 -2.312 3.6876 14.688
## nodematch.race..wa.O -49.89 -15.890 2.110 20.1103 55.110
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.2647565608
## nodefactor.race..wa.B 0.2647566 1.0000000000
## nodefactor.race..wa.H 0.3537234 -0.0008631546
## nodematch.race..wa.B 0.1056490 0.5697787625
## nodematch.race..wa.H 0.1578961 0.0011058680
## nodematch.race..wa.O 0.8055371 -0.0768092727
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.3537233612 0.105649021
## nodefactor.race..wa.B -0.0008631546 0.569778763
## nodefactor.race..wa.H 1.0000000000 0.002158381
## nodematch.race..wa.B 0.0021583813 1.000000000
## nodematch.race..wa.H 0.6423118498 -0.004620620
## nodematch.race..wa.O -0.0757747755 0.027413705
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.157896145 0.80553714
## nodefactor.race..wa.B 0.001105868 -0.07680927
## nodefactor.race..wa.H 0.642311850 -0.07577478
## nodematch.race..wa.B -0.004620620 0.02741370
## nodematch.race..wa.H 1.000000000 0.06691933
## nodematch.race..wa.O 0.066919332 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.016519386 0.017304224 0.063025775
## Lag 2e+05 -0.004136813 0.006852805 0.028877959
## Lag 3e+05 -0.006910188 -0.014316147 0.003086294
## Lag 4e+05 0.024185191 -0.012404451 0.013000294
## Lag 5e+05 -0.001100995 -0.013426432 -0.021914577
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.05372021 0.202676549 0.010609396
## Lag 2e+05 0.02039436 0.044841247 0.005967625
## Lag 3e+05 -0.03397172 -0.009542895 -0.005524379
## Lag 4e+05 0.01847732 0.003741272 0.013000612
## Lag 5e+05 0.01062949 0.015806015 0.023326138
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.00000000 1.0000000000 1.000000000
## Lag 1e+05 0.03170898 0.0220538974 0.042247941
## Lag 2e+05 -0.01242839 0.0073330014 0.006177381
## Lag 3e+05 0.02220543 0.0172176533 0.024663497
## Lag 4e+05 0.01848319 -0.0005187285 0.022241583
## Lag 5e+05 -0.01827191 0.0296952709 -0.024114778
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.045350669 0.18081058 0.01514566
## Lag 2e+05 0.004847473 0.02671978 -0.01544780
## Lag 3e+05 -0.019326394 0.01937799 0.01314423
## Lag 4e+05 -0.016419363 0.01268967 0.02710092
## Lag 5e+05 0.008108552 -0.02253251 -0.02062901
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 0.0093765692 0.0239162984 0.070469479
## Lag 2e+05 0.0334365706 -0.0424366592 -0.004885092
## Lag 3e+05 0.0284832287 -0.0104428208 0.030727177
## Lag 4e+05 0.0006965439 -0.0092699727 -0.001394110
## Lag 5e+05 -0.0318239237 0.0008410848 0.004204452
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.04712306 0.175831092 0.008090358
## Lag 2e+05 0.01680676 -0.008034971 0.016527894
## Lag 3e+05 -0.02366084 0.028664430 0.014421543
## Lag 4e+05 0.01499158 0.008492331 0.005638062
## Lag 5e+05 0.01985874 -0.018280297 -0.029428361
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.005779165 -0.010551662 0.075782084
## Lag 2e+05 -0.003340064 0.005274312 0.006408043
## Lag 3e+05 0.014690284 -0.011892595 0.009221204
## Lag 4e+05 -0.004360978 -0.011412664 0.013878039
## Lag 5e+05 -0.011315547 0.010612762 -0.026012898
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.048291142 0.21533093 0.007813368
## Lag 2e+05 -0.002420700 0.05387946 0.002956265
## Lag 3e+05 -0.012607722 0.02232768 0.002105551
## Lag 4e+05 0.018240396 0.01297915 0.001855877
## Lag 5e+05 -0.008470551 -0.00127063 -0.024432579
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.00000000 1.0000000000
## Lag 1e+05 0.0081198179 0.03475842 0.0590434404
## Lag 2e+05 -0.0110171355 0.01091370 -0.0015956971
## Lag 3e+05 -0.0021593022 -0.01593856 0.0082997100
## Lag 4e+05 0.0001253672 -0.01995427 0.0053330757
## Lag 5e+05 -0.0153044350 -0.01296706 0.0007583651
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.040993844 0.164998869 0.009889971
## Lag 2e+05 0.004827850 0.013497689 0.002420316
## Lag 3e+05 -0.003305413 -0.003800739 0.010790474
## Lag 4e+05 -0.010429687 0.018743993 0.009938880
## Lag 5e+05 0.008292257 0.014301661 -0.007632175
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0175959108 0.026596425 0.040812437
## Lag 2e+05 0.0005891317 0.008209009 0.015502075
## Lag 3e+05 0.0055824580 -0.013535659 0.032977470
## Lag 4e+05 -0.0085849731 -0.029434353 0.053672469
## Lag 5e+05 -0.0160597982 -0.018208719 0.006579775
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.060941203 0.192550191 0.0104338453
## Lag 2e+05 -0.005167953 0.024864067 -0.0005054795
## Lag 3e+05 0.007097300 0.011937740 0.0238448993
## Lag 4e+05 -0.023324822 0.022254873 -0.0157308982
## Lag 5e+05 -0.016148574 0.006550317 -0.0183572028
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0306666136 -0.002572472 0.075778210
## Lag 2e+05 -0.0035071762 0.019391538 0.040134636
## Lag 3e+05 0.0001315622 0.009686850 -0.004056141
## Lag 4e+05 -0.0253067829 -0.024663165 -0.002224635
## Lag 5e+05 -0.0099361363 0.017542824 0.001066214
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.030944645 0.180244300 -0.00768810
## Lag 2e+05 0.043555486 0.053516561 0.01028223
## Lag 3e+05 0.011514849 -0.022485054 -0.00168595
## Lag 4e+05 0.009365457 -0.015999157 -0.03495420
## Lag 5e+05 -0.014477900 0.006433953 -0.02647767
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.007608538 -0.013515705 0.060294210
## Lag 2e+05 -0.008574406 -0.014774737 0.023114665
## Lag 3e+05 -0.022133905 0.012407256 -0.006987887
## Lag 4e+05 -0.005534045 0.021201057 0.009182523
## Lag 5e+05 0.008737817 0.003476892 -0.013135535
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.014784872 0.201416996 0.005211081
## Lag 2e+05 0.022305515 0.031037942 -0.006495462
## Lag 3e+05 0.015088321 -0.003656476 -0.019681934
## Lag 4e+05 0.023702519 0.001526153 0.006654373
## Lag 5e+05 -0.003175929 0.015592441 0.001468278
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2007 1.3501 -0.2650
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.0127 -0.9465 -0.4797
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8409182 0.1769982 0.7909989
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3111912 0.3438809 0.6314456
## Joint P-value (lower = worse): 0.6906256 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.5005 0.2115 -0.3640
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3874 0.3293 2.0011
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.13349632 0.83247545 0.71587472
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.69847620 0.74195696 0.04538503
## Joint P-value (lower = worse): 0.6663923 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4341 1.2091 -0.6912
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.0616 -1.6591 0.2075
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.66425086 0.22660629 0.48941343
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.28841159 0.09710331 0.83560462
## Joint P-value (lower = worse): 0.6852257 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.4306 -0.2211 0.3419
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.6349 -0.3137 -0.8138
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6667486 0.8250033 0.7324084
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.5255087 0.7537536 0.4157744
## Joint P-value (lower = worse): 0.9569717 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.2616 -0.7943 1.7773
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.8380 0.8732 -1.9509
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.20710786 0.42704384 0.07552511
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.40204739 0.38257448 0.05106607
## Joint P-value (lower = worse): 0.2838306 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.2802 -0.2859 -1.3265
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.2323 -0.6576 1.9009
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.20049169 0.77496789 0.18466895
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.21783682 0.51081467 0.05731461
## Joint P-value (lower = worse): 0.3574145 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.4178 -1.6539 -2.9350
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1030 -1.9992 0.3531
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.156240614 0.098144209 0.003336004
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.917982546 0.045581922 0.724014814
## Joint P-value (lower = worse): 0.07356876 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.94485 -0.06226 0.09763
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.02337 -0.02246 0.78247
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3447380 0.9503595 0.9222285
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3061328 0.9820830 0.4339404
## Joint P-value (lower = worse): 0.902809 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -1.64923 28.930 0.16703 0.16646
## nodefactor.deg.pers.1 0.15100 17.404 0.10048 0.10148
## nodefactor.deg.pers.2 -0.03153 18.962 0.10947 0.10947
## nodefactor.race..wa.B 2.17023 12.600 0.07275 0.07407
## nodefactor.race..wa.H 2.43347 17.853 0.10307 0.10830
## nodematch.race..wa.B -2.16982 4.882 0.02819 0.03045
## nodematch.race..wa.H -2.17980 8.783 0.05071 0.06055
## nodematch.race..wa.O 2.52408 26.336 0.15205 0.15215
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -58.50 -21.500 -1.500 17.5000 56.500
## nodefactor.deg.pers.1 -34.00 -12.000 0.000 12.0000 34.000
## nodefactor.deg.pers.2 -37.00 -13.000 0.000 13.0000 38.000
## nodefactor.race..wa.B -22.00 -5.997 2.003 11.0032 27.003
## nodefactor.race..wa.H -32.98 -9.978 2.022 14.0220 38.022
## nodematch.race..wa.B -11.18 -5.179 -2.179 0.8213 7.821
## nodematch.race..wa.H -19.31 -8.312 -2.312 3.6876 15.688
## nodematch.race..wa.O -48.89 -14.890 2.110 20.1103 54.110
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.39296376
## nodefactor.deg.pers.1 0.3929638 1.00000000
## nodefactor.deg.pers.2 0.4283944 0.05461637
## nodefactor.race..wa.B 0.2717870 0.08691547
## nodefactor.race..wa.H 0.3518713 0.15046209
## nodematch.race..wa.B 0.1087730 0.03388839
## nodematch.race..wa.H 0.1628196 0.08427869
## nodematch.race..wa.O 0.8044073 0.32248149
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42839445 0.271787036
## nodefactor.deg.pers.1 0.05461637 0.086915473
## nodefactor.deg.pers.2 1.00000000 0.103750036
## nodefactor.race..wa.B 0.10375004 1.000000000
## nodefactor.race..wa.H 0.13956435 -0.005741791
## nodematch.race..wa.B 0.03703048 0.562516465
## nodematch.race..wa.H 0.05654604 -0.006523265
## nodematch.race..wa.O 0.35206939 -0.073875076
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 3.518713e-01 1.087730e-01
## nodefactor.deg.pers.1 1.504621e-01 3.388839e-02
## nodefactor.deg.pers.2 1.395644e-01 3.703048e-02
## nodefactor.race..wa.B -5.741791e-03 5.625165e-01
## nodefactor.race..wa.H 1.000000e+00 9.987202e-05
## nodematch.race..wa.B 9.987202e-05 1.000000e+00
## nodematch.race..wa.H 6.463113e-01 -7.430724e-03
## nodematch.race..wa.O -7.305191e-02 3.319868e-02
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.162819621 0.80440732
## nodefactor.deg.pers.1 0.084278693 0.32248149
## nodefactor.deg.pers.2 0.056546036 0.35206939
## nodefactor.race..wa.B -0.006523265 -0.07387508
## nodefactor.race..wa.H 0.646311317 -0.07305191
## nodematch.race..wa.B -0.007430724 0.03319868
## nodematch.race..wa.H 1.000000000 0.07596680
## nodematch.race..wa.O 0.075966804 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.008752603 0.019228030 0.0006564403
## Lag 2e+05 -0.019271518 -0.023798195 0.0170813001
## Lag 3e+05 -0.006083547 0.019998568 -0.0016990287
## Lag 4e+05 0.007772439 -0.012321003 0.0105806790
## Lag 5e+05 -0.036990300 -0.001257184 -0.0059260861
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.001671103 0.04673924 0.025219897
## Lag 2e+05 -0.002154491 -0.01197586 0.006103509
## Lag 3e+05 0.004660132 0.03860628 0.036839479
## Lag 4e+05 0.021937532 0.02010942 0.047978270
## Lag 5e+05 -0.021556767 0.01218959 -0.003853325
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.190668059 0.027479581
## Lag 2e+05 0.025799713 -0.019159629
## Lag 3e+05 -0.003472251 -0.030656618
## Lag 4e+05 -0.016311219 -0.004171642
## Lag 5e+05 -0.006070304 -0.013434941
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.010697758 -0.0001182347 -0.0190597451
## Lag 2e+05 -0.005344925 -0.0070188385 0.0042361600
## Lag 3e+05 0.041323703 0.0110430646 -0.0001722969
## Lag 4e+05 -0.010524758 0.0158358889 -0.0132680951
## Lag 5e+05 0.009058742 0.0037857708 0.0024998555
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.037369540 0.053120908 0.056002346
## Lag 2e+05 0.006843422 -0.015382102 0.001157155
## Lag 3e+05 0.020262985 -0.023842251 -0.009688572
## Lag 4e+05 -0.017868941 0.014472006 -0.012100356
## Lag 5e+05 0.015023677 -0.007792651 -0.008210095
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.175695122 -0.01284143
## Lag 2e+05 0.006999300 -0.01414330
## Lag 3e+05 0.009109225 0.04393157
## Lag 4e+05 0.001466106 -0.01242009
## Lag 5e+05 -0.021482677 0.01687529
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.005746642 -0.012702490 0.001768126
## Lag 2e+05 -0.019932684 -0.004405447 0.003480485
## Lag 3e+05 -0.015371414 -0.006551598 -0.012125291
## Lag 4e+05 0.005280698 0.009456580 -0.035881670
## Lag 5e+05 -0.023488759 0.006772772 -0.016418177
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.008271688 0.067931804 0.0549993076
## Lag 2e+05 -0.022770391 0.001554890 0.0136730030
## Lag 3e+05 0.018125571 -0.004813337 0.0006656391
## Lag 4e+05 -0.011872193 0.021086662 -0.0002850301
## Lag 5e+05 -0.010658084 0.016924486 0.0136633996
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.182023395 0.010250578
## Lag 2e+05 0.033682705 -0.013749605
## Lag 3e+05 0.007563420 -0.014074040
## Lag 4e+05 0.009884538 -0.003169226
## Lag 5e+05 -0.013710888 -0.025429039
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.012371530 -0.005058543 0.0020863377
## Lag 2e+05 0.030253006 -0.030174096 0.0004201859
## Lag 3e+05 -0.023750487 0.004536323 0.0097089427
## Lag 4e+05 -0.020740279 0.005583606 -0.0059485661
## Lag 5e+05 -0.001867668 -0.013945195 -0.0105491899
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.029588310 0.06420881 0.082514375
## Lag 2e+05 -0.008648046 0.01773410 0.006040380
## Lag 3e+05 -0.023061402 -0.01056061 0.002003169
## Lag 4e+05 -0.003894681 -0.02919819 -0.028227305
## Lag 5e+05 0.008621708 -0.02743845 0.001207147
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.181251234 0.008820829
## Lag 2e+05 0.063312277 0.030487312
## Lag 3e+05 0.009355647 -0.030390897
## Lag 4e+05 -0.024663789 -0.008071894
## Lag 5e+05 -0.032638001 0.010697883
## Chain 5
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.018898741 0.022547819 0.003889324
## Lag 2e+05 0.020274373 -0.002996416 0.002225064
## Lag 3e+05 -0.008545535 -0.006890455 0.012953124
## Lag 4e+05 0.013538550 -0.014543236 0.003377963
## Lag 5e+05 -0.016983365 -0.007886537 -0.013424982
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.010484700 0.062890685 0.05691356
## Lag 2e+05 0.007362897 0.026887969 0.02876054
## Lag 3e+05 0.002523273 -0.039285525 -0.01335445
## Lag 4e+05 -0.010673506 -0.002807968 0.02072323
## Lag 5e+05 -0.002117223 0.009522715 0.00474244
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.20375649 0.008672935
## Lag 2e+05 0.02571490 0.018949735
## Lag 3e+05 -0.04814642 -0.026741655
## Lag 4e+05 -0.01896733 0.017671168
## Lag 5e+05 -0.01499627 -0.022926062
## Chain 6
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.009109257 0.010495020 0.007956074
## Lag 2e+05 0.001472701 0.040634935 -0.006350537
## Lag 3e+05 0.006201858 -0.008927817 0.001732861
## Lag 4e+05 0.001834809 -0.008998462 -0.005017495
## Lag 5e+05 0.003893267 -0.009007922 -0.009645698
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0307391436 0.082889669 0.073428246
## Lag 2e+05 -0.0124878115 0.014949979 0.010192826
## Lag 3e+05 0.0001052228 0.026791792 -0.025156531
## Lag 4e+05 0.0167301897 0.009903798 0.011027823
## Lag 5e+05 0.0023612081 0.002515532 -0.002080345
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.192324014 -0.0060483765
## Lag 2e+05 0.029353116 -0.0009158723
## Lag 3e+05 0.020080320 0.0055933199
## Lag 4e+05 -0.003683553 -0.0105290115
## Lag 5e+05 -0.019684373 -0.0081958620
## Chain 7
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.005096332 0.025493447 -0.007676836
## Lag 2e+05 -0.005489997 0.007289568 0.003478120
## Lag 3e+05 0.017654406 -0.023556417 0.006255663
## Lag 4e+05 0.020597661 0.006049256 -0.001102295
## Lag 5e+05 0.022602322 -0.009939594 0.023066007
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.031784465 0.0430468827 0.07539525
## Lag 2e+05 -0.018745677 -0.0004698454 0.03526816
## Lag 3e+05 -0.005950322 -0.0308063434 0.00816233
## Lag 4e+05 0.036381817 0.0002043636 0.01720017
## Lag 5e+05 -0.005699507 0.0157061583 -0.01228428
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.0000000000
## Lag 1e+05 0.1558428180 -0.0001961639
## Lag 2e+05 0.0237604682 -0.0059886316
## Lag 3e+05 -0.0006294228 0.0259481869
## Lag 4e+05 0.0057267178 0.0214249884
## Lag 5e+05 -0.0233835344 0.0228193497
## Chain 8
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.006869288 0.007038851 0.005831157
## Lag 2e+05 -0.011398263 -0.012982839 -0.015520681
## Lag 3e+05 -0.002598932 0.005553033 -0.029045748
## Lag 4e+05 0.019492107 -0.012750133 0.014933520
## Lag 5e+05 -0.004375590 -0.016457340 0.002095295
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000e+00 1.000000000 1.000000000
## Lag 1e+05 1.099943e-02 0.052269640 0.083729970
## Lag 2e+05 -6.652445e-03 0.007123053 -0.003486957
## Lag 3e+05 2.299394e-02 -0.036017638 -0.003877512
## Lag 4e+05 -1.689457e-05 -0.009795525 0.012235793
## Lag 5e+05 -6.031556e-03 -0.016328633 0.012627583
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.178373512 -0.011269106
## Lag 2e+05 0.047079012 0.002388994
## Lag 3e+05 -0.013091584 0.002807671
## Lag 4e+05 -0.025336573 0.018222642
## Lag 5e+05 -0.007292732 -0.002810882
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.03568 1.51188 -0.16927
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.77239 -0.93972 0.03346
## nodematch.race..wa.H nodematch.race..wa.O
## -1.19308 -0.18921
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.9715368 0.1305650 0.8655882
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.4398860 0.3473597 0.9733050
## nodematch.race..wa.H nodematch.race..wa.O
## 0.2328384 0.8499299
## Joint P-value (lower = worse): 0.7722156 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.27935 -0.06739 -0.76275
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## -0.24010 0.43825 -1.27736
## nodematch.race..wa.H nodematch.race..wa.O
## -0.66195 -0.80142
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.7799762 0.9462702 0.4456123
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.8102508 0.6612030 0.2014766
## nodematch.race..wa.H nodematch.race..wa.O
## 0.5080057 0.4228894
## Joint P-value (lower = worse): 0.8158355 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.50275 0.07477 1.83482
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.41527 -0.61573 -0.57168
## nodematch.race..wa.H nodematch.race..wa.O
## -0.56053 1.53715
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.13290290 0.94039903 0.06653209
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.67794118 0.53807013 0.56753595
## nodematch.race..wa.H nodematch.race..wa.O
## 0.57512087 0.12425637
## Joint P-value (lower = worse): 0.6407243 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.02398 -1.18162 1.31616
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 1.14171 -0.73987 0.94594
## nodematch.race..wa.H nodematch.race..wa.O
## -0.61694 -0.09553
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.9808698 0.2373570 0.1881195
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.2535732 0.4593776 0.3441771
## nodematch.race..wa.H nodematch.race..wa.O
## 0.5372775 0.9238938
## Joint P-value (lower = worse): 0.720598 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.95769 0.76068 0.53151
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## -0.13792 0.03540 0.02277
## nodematch.race..wa.H nodematch.race..wa.O
## -1.46254 1.30968
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.05026688 0.44685043 0.59506201
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.89030367 0.97176205 0.98183750
## nodematch.race..wa.H nodematch.race..wa.O
## 0.14359447 0.19030487
## Joint P-value (lower = worse): 0.412635 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.3954 1.0232 -0.2737
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.8373 -0.7390 -0.6287
## nodematch.race..wa.H nodematch.race..wa.O
## -0.1005 -0.4319
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.6925830 0.3062140 0.7843004
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.4024087 0.4599071 0.5295564
## nodematch.race..wa.H nodematch.race..wa.O
## 0.9199855 0.6658041
## Joint P-value (lower = worse): 0.7073712 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.9267 1.2938 2.3302
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 1.1376 -0.1476 -0.6048
## nodematch.race..wa.H nodematch.race..wa.O
## 1.5008 1.9939
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.05401854 0.19572163 0.01979509
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.25529806 0.88266652 0.54531298
## nodematch.race..wa.H nodematch.race..wa.O
## 0.13340847 0.04616803
## Joint P-value (lower = worse): 0.05175809 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.91068 -0.56593 -0.54818
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.58966 -1.83451 -0.02352
## nodematch.race..wa.H nodematch.race..wa.O
## -2.11509 1.21750
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.36246212 0.57143870 0.58357081
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.55542106 0.06657767 0.98123730
## nodematch.race..wa.H nodematch.race..wa.O
## 0.03442239 0.22341349
## Joint P-value (lower = worse): 0.385835 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 5
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -2.4174 28.874 0.16670 0.17064
## nodefactor.deg.pers.1 0.2471 17.360 0.10023 0.10243
## nodefactor.deg.pers.2 0.1651 18.882 0.10902 0.10793
## nodefactor.race..wa.B 2.2759 12.534 0.07236 0.07314
## nodefactor.race..wa.H 2.1733 17.718 0.10229 0.10894
## nodefactor.region.EW 0.1258 16.114 0.09303 0.09388
## nodefactor.region.OW -0.3981 29.172 0.16842 0.16866
## nodematch.race..wa.B -2.2905 4.875 0.02814 0.03013
## nodematch.race..wa.H -2.0121 8.669 0.05005 0.06135
## nodematch.race..wa.O 1.9575 26.402 0.15243 0.15536
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -58.50 -22.500 -2.5000 16.5000 54.500
## nodefactor.deg.pers.1 -34.00 -11.000 0.0000 12.0000 34.000
## nodefactor.deg.pers.2 -37.00 -12.000 0.0000 13.0000 37.000
## nodefactor.race..wa.B -22.00 -5.997 2.0032 11.0032 27.003
## nodefactor.race..wa.H -31.98 -9.978 2.0220 14.0220 37.022
## nodefactor.region.EW -31.56 -10.561 0.4392 11.4392 31.439
## nodefactor.region.OW -57.13 -20.131 -0.1306 19.1194 56.869
## nodematch.race..wa.B -11.18 -5.179 -2.1787 0.8213 7.821
## nodematch.race..wa.H -19.31 -7.312 -2.3124 3.6876 14.688
## nodematch.race..wa.O -48.89 -15.890 2.1103 20.1103 54.110
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.38920201
## nodefactor.deg.pers.1 0.3892020 1.00000000
## nodefactor.deg.pers.2 0.4315629 0.04625543
## nodefactor.race..wa.B 0.2608158 0.09189919
## nodefactor.race..wa.H 0.3501165 0.14821436
## nodefactor.region.EW 0.3683386 0.16693475
## nodefactor.region.OW 0.6236113 0.22454589
## nodematch.race..wa.B 0.1046966 0.03169349
## nodematch.race..wa.H 0.1594940 0.07111244
## nodematch.race..wa.O 0.8065444 0.31174861
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.43156287 0.2608158221
## nodefactor.deg.pers.1 0.04625543 0.0918991881
## nodefactor.deg.pers.2 1.00000000 0.0933312678
## nodefactor.race..wa.B 0.09333127 1.0000000000
## nodefactor.race..wa.H 0.14437082 -0.0126352451
## nodefactor.region.EW 0.16008876 0.0386719268
## nodefactor.region.OW 0.25718348 0.1270393357
## nodematch.race..wa.B 0.03510175 0.5570690233
## nodematch.race..wa.H 0.06650089 -0.0007580856
## nodematch.race..wa.O 0.35908929 -0.0784044756
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.350116474 0.36833860
## nodefactor.deg.pers.1 0.148214362 0.16693475
## nodefactor.deg.pers.2 0.144370823 0.16008876
## nodefactor.race..wa.B -0.012635245 0.03867193
## nodefactor.race..wa.H 1.000000000 0.25270491
## nodefactor.region.EW 0.252704913 1.00000000
## nodefactor.region.OW 0.206448288 0.05770138
## nodematch.race..wa.B -0.001412931 0.01199184
## nodematch.race..wa.H 0.637817567 0.13191317
## nodematch.race..wa.O -0.073009634 0.26040657
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.62361129 0.104696584
## nodefactor.deg.pers.1 0.22454589 0.031693494
## nodefactor.deg.pers.2 0.25718348 0.035101747
## nodefactor.race..wa.B 0.12703934 0.557069023
## nodefactor.race..wa.H 0.20644829 -0.001412931
## nodefactor.region.EW 0.05770138 0.011991836
## nodefactor.region.OW 1.00000000 0.043664364
## nodematch.race..wa.B 0.04366436 1.000000000
## nodematch.race..wa.H 0.09053518 0.004155459
## nodematch.race..wa.O 0.52092798 0.036986142
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.1594939792 0.80654441
## nodefactor.deg.pers.1 0.0711124369 0.31174861
## nodefactor.deg.pers.2 0.0665008920 0.35908929
## nodefactor.race..wa.B -0.0007580856 -0.07840448
## nodefactor.race..wa.H 0.6378175672 -0.07300963
## nodefactor.region.EW 0.1319131662 0.26040657
## nodefactor.region.OW 0.0905351783 0.52092798
## nodematch.race..wa.B 0.0041554585 0.03698614
## nodematch.race..wa.H 1.0000000000 0.07587968
## nodematch.race..wa.O 0.0758796776 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.001049806 -0.028478905 -0.005672202
## Lag 2e+05 -0.039497494 -0.011966225 -0.022325522
## Lag 3e+05 0.018347509 0.003504927 -0.005642327
## Lag 4e+05 -0.005827387 0.003954342 -0.007101611
## Lag 5e+05 -0.007702701 -0.014145071 0.021403180
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.003750884 0.029700751 0.0018448784
## Lag 2e+05 -0.026698597 0.009572208 -0.0009416475
## Lag 3e+05 -0.008801070 0.024189849 0.0238007061
## Lag 4e+05 0.001239073 -0.007585066 0.0004980398
## Lag 5e+05 -0.012352484 0.004127481 0.0176534745
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.01310701 0.052610802 0.17990509
## Lag 2e+05 -0.02601239 0.007609806 0.04675860
## Lag 3e+05 -0.02483874 -0.009572847 0.02692782
## Lag 4e+05 0.01820572 0.006378743 -0.02596679
## Lag 5e+05 -0.03912374 -0.013125159 -0.02291148
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0148688080
## Lag 2e+05 -0.0281587757
## Lag 3e+05 0.0004260314
## Lag 4e+05 0.0037552797
## Lag 5e+05 0.0019121699
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0006277275 0.006253874 0.038817679
## Lag 2e+05 0.0081645791 0.025140475 -0.012193937
## Lag 3e+05 0.0397651132 0.019229896 -0.015194345
## Lag 4e+05 0.0283929187 0.036593033 -0.007596629
## Lag 5e+05 0.0486225609 0.030858222 -0.020535525
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.043955781 0.05102087 0.007626162
## Lag 2e+05 -0.016223983 0.01552412 -0.003143505
## Lag 3e+05 0.017241617 -0.01344778 -0.014030363
## Lag 4e+05 0.016678137 0.01831750 -0.001125035
## Lag 5e+05 0.006373996 -0.01296563 -0.019953405
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.015915952 0.058636920 0.212948507
## Lag 2e+05 -0.010484841 -0.002589235 0.051510219
## Lag 3e+05 0.036306268 0.033242670 0.024646104
## Lag 4e+05 0.008710409 -0.007650838 -0.007886587
## Lag 5e+05 0.007750022 0.011907151 -0.009332105
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0022493610
## Lag 2e+05 -0.0007049176
## Lag 3e+05 0.0367553292
## Lag 4e+05 0.0185992582
## Lag 5e+05 0.0293059516
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.003741188 0.015298817 -0.009677935
## Lag 2e+05 0.046833384 -0.009200224 -0.025592757
## Lag 3e+05 0.018265805 -0.003842867 0.006622575
## Lag 4e+05 -0.008338919 0.011359737 0.019763599
## Lag 5e+05 -0.032124750 -0.034304239 -0.038806258
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.006634762 0.0676560654 0.028796986
## Lag 2e+05 0.015660030 0.0277743244 0.008402996
## Lag 3e+05 -0.010680199 -0.0002717566 0.033119585
## Lag 4e+05 -0.007197192 -0.0060549420 -0.004357217
## Lag 5e+05 -0.009538568 -0.0172293109 -0.026678894
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.002403779 0.033340242 0.194970239
## Lag 2e+05 0.016344411 0.041199224 0.035067617
## Lag 3e+05 -0.004064261 -0.023666068 0.031848960
## Lag 4e+05 -0.037027587 0.009525003 0.001122687
## Lag 5e+05 -0.007076284 -0.012450715 -0.018689601
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.003132856
## Lag 2e+05 -0.002291626
## Lag 3e+05 0.001808988
## Lag 4e+05 -0.009839553
## Lag 5e+05 -0.009278227
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.027738521 0.01136658 0.007472435
## Lag 2e+05 -0.002293374 -0.02656879 0.002642186
## Lag 3e+05 -0.009436103 0.01278375 -0.027526179
## Lag 4e+05 0.010044563 -0.01721805 0.028670077
## Lag 5e+05 0.009357784 -0.02321401 0.017304527
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.031935591 0.066377989 0.0129912684
## Lag 2e+05 -0.011868891 0.007658114 0.0191680867
## Lag 3e+05 0.015816824 0.021566521 -0.0002266523
## Lag 4e+05 -0.007499524 0.018675762 0.0126467584
## Lag 5e+05 0.000675882 -0.010547651 0.0350177187
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 -0.005491499 0.0866003333 0.19780660
## Lag 2e+05 -0.020422301 -0.0102266964 0.04288614
## Lag 3e+05 -0.005092385 0.0009030007 0.01441477
## Lag 4e+05 0.005015785 -0.0205809099 -0.01062330
## Lag 5e+05 0.026163667 0.0038120877 -0.01339582
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.017249442
## Lag 2e+05 -0.002796974
## Lag 3e+05 0.014455339
## Lag 4e+05 0.012997695
## Lag 5e+05 0.015754334
## Chain 5
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.004568370 0.016948720 -0.016889539
## Lag 2e+05 -0.016679709 -0.009102403 -0.029127595
## Lag 3e+05 0.013020125 -0.008114648 0.009070979
## Lag 4e+05 0.006803005 0.013953889 -0.003579536
## Lag 5e+05 -0.016995002 -0.017422838 0.005255772
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.020125108 0.05887490 0.009821780
## Lag 2e+05 0.004975566 0.01065868 -0.019754080
## Lag 3e+05 0.002784859 0.01202140 -0.026648531
## Lag 4e+05 -0.013899384 0.02831271 -0.009762018
## Lag 5e+05 -0.012253916 -0.01502716 -0.009938553
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.01285476 0.065451551 0.19279767
## Lag 2e+05 -0.01554584 0.001496019 0.05275444
## Lag 3e+05 0.00944926 -0.014140632 0.01793667
## Lag 4e+05 -0.01282132 -0.009620996 0.02603839
## Lag 5e+05 -0.02484438 0.006903983 0.03022297
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.009847678
## Lag 2e+05 -0.020254346
## Lag 3e+05 0.024423972
## Lag 4e+05 0.012393142
## Lag 5e+05 -0.005749656
## Chain 6
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.025512829 0.0185239189 -0.020617830
## Lag 2e+05 0.016762751 -0.0002942647 -0.034284650
## Lag 3e+05 -0.015219888 0.0203216091 -0.022058233
## Lag 4e+05 0.005982033 0.0251427045 0.009259874
## Lag 5e+05 -0.005076096 -0.0053059041 -0.040920351
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.018118508 0.075499402 0.01445953
## Lag 2e+05 -0.009069013 0.015650105 0.02595361
## Lag 3e+05 -0.001758302 -0.007933772 -0.01292903
## Lag 4e+05 0.003451277 0.004356847 0.00378161
## Lag 5e+05 0.005590283 -0.027026383 -0.01594278
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.030003359 0.058192754 0.188001879
## Lag 2e+05 -0.007024628 -0.008018904 0.028406665
## Lag 3e+05 -0.012885326 0.028537447 0.015382544
## Lag 4e+05 0.018883858 -0.010598214 0.001843182
## Lag 5e+05 0.007995351 0.003278633 -0.013645727
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.027975940
## Lag 2e+05 0.026655080
## Lag 3e+05 -0.017745941
## Lag 4e+05 0.003079083
## Lag 5e+05 0.008566155
## Chain 7
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.000000e+00 1.0000000000
## Lag 1e+05 0.03145656 2.795297e-02 0.0236810064
## Lag 2e+05 0.01040132 9.234836e-03 -0.0002621985
## Lag 3e+05 -0.02164488 2.278715e-02 -0.0252862123
## Lag 4e+05 -0.02010451 2.893420e-03 -0.0088712187
## Lag 5e+05 0.01051864 -9.261249e-05 0.0184195682
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.017327785 0.068949989 0.017800784
## Lag 2e+05 -0.004155202 0.001349412 -0.002856958
## Lag 3e+05 -0.033302778 -0.029239852 -0.002857431
## Lag 4e+05 0.025646332 -0.016309873 0.009369328
## Lag 5e+05 0.009261599 0.030900937 0.034858846
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.02553139 0.065296956 0.197568239
## Lag 2e+05 -0.01649843 0.010843449 0.046797511
## Lag 3e+05 -0.01377140 -0.030590804 0.005528565
## Lag 4e+05 -0.01338507 0.013258773 0.020987575
## Lag 5e+05 0.01148116 0.002268468 0.018986126
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.026811272
## Lag 2e+05 -0.008549901
## Lag 3e+05 -0.029742325
## Lag 4e+05 -0.022582580
## Lag 5e+05 0.019698304
## Chain 8
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.006977888 0.0007445388 -0.016761577
## Lag 2e+05 -0.007231029 -0.0055756346 0.011502687
## Lag 3e+05 -0.003276919 -0.0173546777 -0.005546904
## Lag 4e+05 -0.001947098 -0.0023433084 -0.029720977
## Lag 5e+05 0.014270561 -0.0140610904 0.003463717
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.005921685 0.0805253190 0.0002463126
## Lag 2e+05 0.003723512 -0.0056013980 0.0148900909
## Lag 3e+05 0.008547017 -0.0004304767 0.0060599927
## Lag 4e+05 -0.009639112 -0.0189095450 -0.0080202259
## Lag 5e+05 0.012296509 -0.0106714345 -0.0211186203
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0001511049 0.079541392 0.1971588217
## Lag 2e+05 -0.0248934242 0.004784168 0.0324590539
## Lag 3e+05 -0.0149022723 -0.028291105 -0.0076538485
## Lag 4e+05 0.0068390989 -0.027676783 -0.0049169511
## Lag 5e+05 -0.0033314549 -0.009529501 0.0002696863
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.024454371
## Lag 2e+05 -0.005944140
## Lag 3e+05 -0.012051068
## Lag 4e+05 -0.003311608
## Lag 5e+05 0.001627818
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.3632 -0.7893 0.1253
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.6660 -0.6720 -0.1384
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.6898 -0.4301 -0.3662
## nodematch.race..wa.O
## 0.1202
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.7164774 0.4299522 0.9002729
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.5054087 0.5015535 0.8899030
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.4903285 0.6671221 0.7142074
## nodematch.race..wa.O
## 0.9042866
## Joint P-value (lower = worse): 0.9978362 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.9786 1.2046 -0.4787
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.2104 1.3730 -0.3877
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.7010 0.5647 -1.3596
## nodematch.race..wa.O
## -0.6873
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.3277814 0.2283682 0.6321707
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.2261326 0.1697533 0.6982176
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.4832810 0.5722481 0.1739581
## nodematch.race..wa.O
## 0.4918915
## Joint P-value (lower = worse): 0.1991235 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.12968 0.27181 -0.44625
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.47738 0.24686 -0.74424
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.07371 -0.09504 0.34314
## nodematch.race..wa.O
## -1.63934
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.2586119 0.7857677 0.6554191
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.6330885 0.8050152 0.4567318
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.9412416 0.9242795 0.7314923
## nodematch.race..wa.O
## 0.1011417
## Joint P-value (lower = worse): 0.9311008 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.55282 -1.45148 -0.33532
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.37941 0.02449 -2.17140
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -1.89456 -0.58095 0.77782
## nodematch.race..wa.O
## -0.25258
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.58038385 0.14664578 0.73738597
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.70438548 0.98045799 0.02990102
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.05815037 0.56127415 0.43667360
## nodematch.race..wa.O
## 0.80058970
## Joint P-value (lower = worse): 0.2460129 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.32258 1.22109 -0.03025
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -1.33644 0.71713 -0.16281
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.34424 -0.96992 -0.70986
## nodematch.race..wa.O
## -0.67772
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.7470137 0.2220526 0.9758716
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.1814040 0.4732959 0.8706671
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.7306687 0.3320864 0.4777906
## nodematch.race..wa.O
## 0.4979524
## Joint P-value (lower = worse): 0.7100157 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.04674 -0.53744 0.23625
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.28257 0.39418 -0.26489
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.05278 1.84064 0.65116
## nodematch.race..wa.O
## 0.37321
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.96272354 0.59096360 0.81323979
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.77750797 0.69345160 0.79109267
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.95790499 0.06567364 0.51494441
## nodematch.race..wa.O
## 0.70898993
## Joint P-value (lower = worse): 0.7736824 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.68781 0.05365 -0.29850
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.06537 0.37912 0.80015
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.41523 0.65461 0.24082
## nodematch.race..wa.O
## 0.73678
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.4915751 0.9572164 0.7653245
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.9478808 0.7045991 0.4236225
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.6779726 0.5127212 0.8096970
## nodematch.race..wa.O
## 0.4612540
## Joint P-value (lower = worse): 0.9935162 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.4718 0.3774 -1.5370
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 2.1207 -2.6605 0.9492
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.4621 0.6931 -1.5421
## nodematch.race..wa.O
## -0.0287
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.637091483 0.705897736 0.124298233
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.033951121 0.007802187 0.342527570
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.644014125 0.488277394 0.123039375
## nodematch.race..wa.O
## 0.977101373
## Joint P-value (lower = worse): 0.04426427 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 6
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -1.7396 28.675 0.16556 0.16723
## nodefactor.deg.pers.1 0.5422 17.412 0.10053 0.10323
## nodefactor.deg.pers.2 -0.3173 18.852 0.10884 0.11061
## nodefactor.race..wa.B 2.0365 12.444 0.07185 0.07558
## nodefactor.race..wa.H 2.4968 17.599 0.10161 0.12171
## nodefactor.region.EW 0.4809 15.801 0.09123 0.09424
## nodefactor.region.OW 0.1259 29.330 0.16934 0.17307
## nodematch.race..wa.B -2.2835 4.865 0.02809 0.03460
## nodematch.race..wa.H -2.0655 8.699 0.05022 0.07654
## nodematch.race..wa.O 2.5048 26.407 0.15246 0.15411
## absdiff.sqrt.age 0.0810 28.536 0.16475 0.16679
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -58.50 -20.500 -1.50000 17.5000 54.500
## nodefactor.deg.pers.1 -34.00 -11.000 0.00000 12.0000 35.000
## nodefactor.deg.pers.2 -37.00 -13.000 0.00000 12.0000 36.000
## nodefactor.race..wa.B -22.00 -5.997 2.00320 10.0032 26.003
## nodefactor.race..wa.H -31.98 -8.978 2.02200 14.0220 37.022
## nodefactor.region.EW -30.56 -10.561 0.43923 11.4392 31.439
## nodefactor.region.OW -58.13 -19.131 -0.13057 19.8694 56.869
## nodematch.race..wa.B -11.18 -5.179 -2.17872 0.8213 7.821
## nodematch.race..wa.H -18.31 -8.312 -2.31243 3.6876 15.688
## nodematch.race..wa.O -49.89 -14.890 2.11028 20.1103 55.110
## absdiff.sqrt.age -55.15 -19.472 0.06771 19.1786 56.556
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.39996517
## nodefactor.deg.pers.1 0.3999652 1.00000000
## nodefactor.deg.pers.2 0.4259049 0.05367137
## nodefactor.race..wa.B 0.2538942 0.09265997
## nodefactor.race..wa.H 0.3418588 0.15043543
## nodefactor.region.EW 0.3616464 0.15581205
## nodefactor.region.OW 0.6193141 0.22468449
## nodematch.race..wa.B 0.1037485 0.03301298
## nodematch.race..wa.H 0.1504515 0.07253975
## nodematch.race..wa.O 0.8070925 0.32037481
## absdiff.sqrt.age 0.5431607 0.21858183
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42590485 0.253894181
## nodefactor.deg.pers.1 0.05367137 0.092659966
## nodefactor.deg.pers.2 1.00000000 0.088540630
## nodefactor.race..wa.B 0.08854063 1.000000000
## nodefactor.race..wa.H 0.13815911 -0.004539635
## nodefactor.region.EW 0.15815699 0.040950006
## nodefactor.region.OW 0.24732882 0.126905921
## nodematch.race..wa.B 0.03135354 0.557499165
## nodematch.race..wa.H 0.06160943 0.003861636
## nodematch.race..wa.O 0.35475913 -0.088528804
## absdiff.sqrt.age 0.22622389 0.133692253
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.341858791 0.36164644
## nodefactor.deg.pers.1 0.150435431 0.15581205
## nodefactor.deg.pers.2 0.138159111 0.15815699
## nodefactor.race..wa.B -0.004539635 0.04095001
## nodefactor.race..wa.H 1.000000000 0.23129413
## nodefactor.region.EW 0.231294130 1.00000000
## nodefactor.region.OW 0.192038659 0.05669375
## nodematch.race..wa.B 0.001925968 0.01331005
## nodematch.race..wa.H 0.639998740 0.13071048
## nodematch.race..wa.O -0.081913216 0.26477614
## absdiff.sqrt.age 0.191518228 0.19118412
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.61931409 0.1037484567
## nodefactor.deg.pers.1 0.22468449 0.0330129763
## nodefactor.deg.pers.2 0.24732882 0.0313535429
## nodefactor.race..wa.B 0.12690592 0.5574991655
## nodefactor.race..wa.H 0.19203866 0.0019259683
## nodefactor.region.EW 0.05669375 0.0133100464
## nodefactor.region.OW 1.00000000 0.0465494856
## nodematch.race..wa.B 0.04654949 1.0000000000
## nodematch.race..wa.H 0.07761130 -0.0001605015
## nodematch.race..wa.O 0.51886481 0.0328519438
## absdiff.sqrt.age 0.33824247 0.0515356640
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.1504514686 0.80709248
## nodefactor.deg.pers.1 0.0725397454 0.32037481
## nodefactor.deg.pers.2 0.0616094263 0.35475913
## nodefactor.race..wa.B 0.0038616357 -0.08852880
## nodefactor.race..wa.H 0.6399987402 -0.08191322
## nodefactor.region.EW 0.1307104771 0.26477614
## nodefactor.region.OW 0.0776113048 0.51886481
## nodematch.race..wa.B -0.0001605015 0.03285194
## nodematch.race..wa.H 1.0000000000 0.06440933
## nodematch.race..wa.O 0.0644093334 1.00000000
## absdiff.sqrt.age 0.0845970197 0.43653880
## absdiff.sqrt.age
## edges 0.54316072
## nodefactor.deg.pers.1 0.21858183
## nodefactor.deg.pers.2 0.22622389
## nodefactor.race..wa.B 0.13369225
## nodefactor.race..wa.H 0.19151823
## nodefactor.region.EW 0.19118412
## nodefactor.region.OW 0.33824247
## nodematch.race..wa.B 0.05153566
## nodematch.race..wa.H 0.08459702
## nodematch.race..wa.O 0.43653880
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.005293560 0.04580469 -0.019605892
## Lag 2e+05 0.021285733 0.02001586 -0.008767053
## Lag 3e+05 0.007328327 0.01254249 -0.017467294
## Lag 4e+05 0.014131069 -0.01184943 0.016485409
## Lag 5e+05 -0.015391061 -0.02549088 0.005099705
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.071261857 0.121462478 0.006008265
## Lag 2e+05 0.017608654 0.075913234 0.032029541
## Lag 3e+05 -0.006253184 0.026245985 0.034350695
## Lag 4e+05 0.006359347 0.015666757 -0.013799286
## Lag 5e+05 0.042492403 -0.002036662 0.009299024
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.007148178 0.16644005 0.342484122
## Lag 2e+05 -0.003049443 0.04449474 0.145435846
## Lag 3e+05 0.032491506 0.05731573 0.071695262
## Lag 4e+05 0.027910356 0.02988396 0.028621776
## Lag 5e+05 0.031758263 0.01388689 -0.009458021
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.001508030 -0.003105105
## Lag 2e+05 -0.003665394 0.037585708
## Lag 3e+05 0.032103947 0.012358136
## Lag 4e+05 0.009942418 -0.007415915
## Lag 5e+05 -0.022343928 -0.018024290
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.005702368 0.026833648 0.023716076
## Lag 2e+05 0.010588181 0.019131377 0.010839306
## Lag 3e+05 0.008285297 0.005830353 0.015981521
## Lag 4e+05 0.014942467 0.010721085 -0.016408090
## Lag 5e+05 0.034404815 -0.002321470 0.006494392
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.033129751 0.11577337 0.04315336
## Lag 2e+05 0.013714622 0.03359602 0.02074684
## Lag 3e+05 -0.021783203 0.03763816 -0.01377397
## Lag 4e+05 -0.027985825 0.03911414 -0.02030157
## Lag 5e+05 -0.002755909 0.02419932 0.02972016
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.004819800 0.179193182 0.33604039
## Lag 2e+05 -0.002623491 0.059377139 0.17442967
## Lag 3e+05 0.001734381 0.002997265 0.10382879
## Lag 4e+05 0.016512312 0.003086446 0.07568856
## Lag 5e+05 0.017195248 0.003636133 0.04088698
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0343152296 0.013710106
## Lag 2e+05 0.0201490357 -0.001567227
## Lag 3e+05 -0.0007944605 -0.013476206
## Lag 4e+05 0.0190649151 0.017049113
## Lag 5e+05 0.0318063768 0.030502760
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0023127554 0.026779886 0.029821562
## Lag 2e+05 0.0039928305 0.005646198 0.018438065
## Lag 3e+05 -0.0005696995 -0.013989453 -0.011088865
## Lag 4e+05 0.0027787549 -0.038295688 -0.008601104
## Lag 5e+05 0.0118866469 -0.025121489 0.023101688
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.067476853 0.1299366910 0.008497376
## Lag 2e+05 -0.003580596 0.0406808834 0.013688716
## Lag 3e+05 -0.014073147 0.0324685488 -0.009953501
## Lag 4e+05 -0.023600782 0.0009930865 -0.004732366
## Lag 5e+05 -0.040538496 0.0210760051 0.010984170
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.020645462 0.16971141 0.34371370
## Lag 2e+05 -0.001135087 0.03881282 0.17747363
## Lag 3e+05 0.003518142 0.04238478 0.08088470
## Lag 4e+05 0.019942307 -0.01489772 0.02904910
## Lag 5e+05 -0.014509410 -0.04150348 0.02393723
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0001439271 0.003761680
## Lag 2e+05 0.0376418444 0.016824750
## Lag 3e+05 -0.0209856939 0.038637512
## Lag 4e+05 0.0057281231 0.012907778
## Lag 5e+05 -0.0082628028 -0.003680867
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.031117749 0.026666024 0.03739183
## Lag 2e+05 -0.009970223 0.004621065 0.02405590
## Lag 3e+05 -0.003086740 0.006048212 0.01887761
## Lag 4e+05 0.027628775 0.012042929 -0.01135316
## Lag 5e+05 -0.001126760 0.025499386 0.01375181
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 0.0528394081 0.117818497 0.01306985
## Lag 2e+05 0.0024475845 0.067073848 -0.02039454
## Lag 3e+05 0.0246565873 0.009273808 0.02521637
## Lag 4e+05 0.0004341354 -0.001045780 0.02852425
## Lag 5e+05 0.0038299948 0.009331017 0.00698654
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.031094977 0.18780861 0.33724675
## Lag 2e+05 -0.002535764 0.03873053 0.18626463
## Lag 3e+05 -0.014456507 0.02227264 0.09723999
## Lag 4e+05 0.028152111 0.02039556 0.05670532
## Lag 5e+05 0.001613010 0.00694822 0.03213768
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.024361104 0.020415028
## Lag 2e+05 -0.011663159 0.017503493
## Lag 3e+05 -0.009622830 0.016431976
## Lag 4e+05 0.029281952 0.026724881
## Lag 5e+05 0.006630604 -0.009558783
## Chain 5
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.037023763 0.0358414885 0.04168991
## Lag 2e+05 0.020943056 0.0221090322 0.01727855
## Lag 3e+05 0.004763418 -0.0118848498 0.02514273
## Lag 4e+05 -0.005145204 0.0006386401 -0.00381406
## Lag 5e+05 -0.015578149 0.0371103871 -0.01866116
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.051280612 0.11154106 0.037396705
## Lag 2e+05 0.030330474 0.04607259 0.039368975
## Lag 3e+05 0.003328961 0.01660608 0.026593192
## Lag 4e+05 -0.026609274 0.03931004 -0.016167306
## Lag 5e+05 0.002151416 0.01961920 0.005479975
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.018199318 0.1964556219 0.31961733
## Lag 2e+05 0.035458599 0.0664757793 0.13206720
## Lag 3e+05 -0.004136924 0.0055495736 0.06228487
## Lag 4e+05 0.007986066 0.0004050579 0.03755850
## Lag 5e+05 0.011113845 -0.0025452172 0.02044208
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.030723539 0.0088327715
## Lag 2e+05 0.005316949 -0.0053238251
## Lag 3e+05 0.012522791 0.0001082466
## Lag 4e+05 -0.005274666 0.0178974829
## Lag 5e+05 -0.004820966 -0.0151301138
## Chain 6
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.008519498 0.0273823711 0.014922439
## Lag 2e+05 0.032607352 0.0067687523 0.017835685
## Lag 3e+05 -0.029688971 -0.0006921469 -0.039210689
## Lag 4e+05 -0.011353922 -0.0139289111 0.006451153
## Lag 5e+05 0.007072129 -0.0321016726 -0.002646642
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.052432149 0.125164533 0.0176374257
## Lag 2e+05 0.021352221 0.042979048 0.0014615935
## Lag 3e+05 0.032228459 0.007710214 0.0003346499
## Lag 4e+05 -0.001710624 -0.009701574 -0.0005798581
## Lag 5e+05 -0.027511115 0.012684979 -0.0028409892
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.028085812 0.157833856 0.36263058
## Lag 2e+05 0.042956515 0.052056930 0.17864497
## Lag 3e+05 -0.005714479 0.033391537 0.08961543
## Lag 4e+05 0.006135763 0.007463110 0.03038678
## Lag 5e+05 0.001245613 0.004149323 0.01466065
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.009989653 0.011278160
## Lag 2e+05 0.024154864 0.020320739
## Lag 3e+05 -0.028116910 0.001744618
## Lag 4e+05 -0.004641422 -0.022476928
## Lag 5e+05 -0.003063877 0.013368985
## Chain 7
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.011333709 0.0004387242 0.0017491655
## Lag 2e+05 -0.007319982 -0.0192388064 0.0098213443
## Lag 3e+05 0.005574105 0.0122862871 0.0006574904
## Lag 4e+05 0.012753676 0.0094488785 0.0049591217
## Lag 5e+05 0.018146661 -0.0057640196 -0.0012615657
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.027290469 0.0997687150 0.017815313
## Lag 2e+05 0.031354836 0.0538139311 0.017187151
## Lag 3e+05 0.004188196 0.0468356690 0.004354206
## Lag 4e+05 0.003690554 0.0016733822 0.009582845
## Lag 5e+05 -0.026388073 0.0008791984 0.007423787
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0160186022 0.206548283 0.317172600
## Lag 2e+05 -0.0257450299 0.080444059 0.160488663
## Lag 3e+05 -0.0072518415 0.036660257 0.084265495
## Lag 4e+05 0.0005570283 -0.001741812 0.051147200
## Lag 5e+05 0.0029493583 -0.016522742 0.008056318
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.00000000
## Lag 1e+05 -0.0137762124 0.00761486
## Lag 2e+05 -0.0074517985 -0.01071302
## Lag 3e+05 0.0018169218 -0.01015128
## Lag 4e+05 -0.0008833292 0.01727140
## Lag 5e+05 0.0226659320 0.02023964
## Chain 8
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.006167298 0.029974348 0.020183847
## Lag 2e+05 -0.005442440 -0.004442949 0.005046315
## Lag 3e+05 -0.002714087 -0.020964491 0.011221175
## Lag 4e+05 0.006183734 -0.008844030 -0.010734423
## Lag 5e+05 -0.031189929 -0.014513607 -0.024812337
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000e+00 1.00000000 1.000000000
## Lag 1e+05 3.488321e-02 0.10877384 0.035992777
## Lag 2e+05 -6.016804e-05 0.04843700 0.005331684
## Lag 3e+05 4.774266e-03 0.04154758 0.017031869
## Lag 4e+05 1.394160e-03 0.01167025 0.020608149
## Lag 5e+05 3.301648e-02 0.01110975 -0.022880475
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.010105580 0.200560144 0.306601117
## Lag 2e+05 0.006094280 0.080352088 0.149244265
## Lag 3e+05 -0.014094653 0.014152647 0.067178918
## Lag 4e+05 -0.004292082 -0.001001567 0.043592985
## Lag 5e+05 -0.027550571 0.016768333 0.009103252
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.001833237 0.0004100609
## Lag 2e+05 0.012516983 -0.0257138714
## Lag 3e+05 -0.003523858 0.0233915355
## Lag 4e+05 0.002086771 0.0114786829
## Lag 5e+05 -0.023818681 -0.0315281184
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.6115 1.8548 -0.6848
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.1251 -0.9310 -0.3712
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 2.0193 1.5306 -1.5868
## nodematch.race..wa.O absdiff.sqrt.age
## 0.3057 0.5037
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.54086004 0.06362077 0.49345726
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.26053015 0.35185217 0.71049090
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.04346055 0.12586506 0.11255927
## nodematch.race..wa.O absdiff.sqrt.age
## 0.75982324 0.61443789
## Joint P-value (lower = worse): 0.1753326 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.7223 0.8729 -0.5022
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.2799 2.7447 0.2370
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.2398 -0.9160 2.2021
## nodematch.race..wa.O absdiff.sqrt.age
## -0.1608 2.1219
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.470098791 0.382718017 0.615545377
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.779541178 0.006057346 0.812653436
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.810481713 0.359647290 0.027655646
## nodematch.race..wa.O absdiff.sqrt.age
## 0.872285369 0.033850009
## Joint P-value (lower = worse): 0.2323312 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.2665 -2.0365 -0.1881
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -1.0442 0.3118 -0.8963
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.6009 -1.3906 0.7861
## nodematch.race..wa.O absdiff.sqrt.age
## 0.6732 1.2129
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.78981785 0.04169969 0.85076023
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.29638638 0.75519185 0.37010586
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.54790404 0.16434307 0.43178463
## nodematch.race..wa.O absdiff.sqrt.age
## 0.50081716 0.22515176
## Joint P-value (lower = worse): 0.318997 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.01504 0.59876 0.18257
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.03015 -0.07265 0.46190
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.85946 0.75310 0.91404
## nodematch.race..wa.O absdiff.sqrt.age
## 1.74969 0.18362
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.31008513 0.54933562 0.85513576
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.97595098 0.94208082 0.64415662
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.39008946 0.45139165 0.36069366
## nodematch.race..wa.O absdiff.sqrt.age
## 0.08017129 0.85431131
## Joint P-value (lower = worse): 0.8909712 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.009625 1.071616 -0.684502
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.608153 -1.843711 0.706592
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.696182 1.233685 0.254572
## nodematch.race..wa.O absdiff.sqrt.age
## 1.406832 0.182161
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.99232029 0.28389248 0.49365833
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.54308578 0.06522530 0.47982035
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.08985139 0.21732035 0.79905405
## nodematch.race..wa.O absdiff.sqrt.age
## 0.15947707 0.85545651
## Joint P-value (lower = worse): 0.04451563 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.4462 0.6401 -0.1260
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.5743 1.2494 0.8125
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.6450 -0.6609 1.6447
## nodematch.race..wa.O absdiff.sqrt.age
## 1.7586 0.2159
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.1481079 0.5220930 0.8997369
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.5657949 0.2115336 0.4164942
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.5189108 0.5086916 0.1000325
## nodematch.race..wa.O absdiff.sqrt.age
## 0.0786495 0.8290902
## Joint P-value (lower = worse): 0.8884442 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.25834 -1.75934 1.58531
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -1.07857 1.94494 1.27107
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.52291 -1.13162 0.06172
## nodematch.race..wa.O absdiff.sqrt.age
## -0.80419 1.14834
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.79614656 0.07851962 0.11289640
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.28077793 0.05178165 0.20370268
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.60103671 0.25779516 0.95078404
## nodematch.race..wa.O absdiff.sqrt.age
## 0.42128826 0.25082764
## Joint P-value (lower = worse): 0.1457331 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.15725 -0.10393 -0.58146
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.15382 0.43965 0.87092
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.89326 1.49813 0.34646
## nodematch.race..wa.O absdiff.sqrt.age
## 0.08083 -1.18821
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.8750439 0.9172246 0.5609334
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.8777511 0.6601936 0.3838001
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.3717202 0.1340988 0.7290002
## nodematch.race..wa.O absdiff.sqrt.age
## 0.9355735 0.2347501
## Joint P-value (lower = worse): 0.499685 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 7
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.9230 28.787 0.16620 0.18593
## nodefactor.deg.pers.1 -0.1031 17.469 0.10086 0.11389
## nodefactor.deg.pers.2 0.0045 18.652 0.10768 0.12771
## nodefactor.race..wa.B 2.4911 12.514 0.07225 0.08535
## nodefactor.race..wa.H 1.4485 17.515 0.10112 0.17080
## nodefactor.region.EW -0.2812 18.227 0.10523 0.16231
## nodefactor.region.OW 0.1298 33.250 0.19197 0.20777
## nodematch.race..wa.B -2.0628 4.858 0.02805 0.04394
## nodematch.race..wa.H -2.3635 8.542 0.04932 0.11964
## nodematch.race..wa.O 3.8377 26.126 0.15084 0.17308
## absdiff.sqrt.age 0.8042 28.515 0.16463 0.18231
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -57.50 -20.500 -1.5000 18.5000 55.500
## nodefactor.deg.pers.1 -34.00 -12.000 0.0000 12.0000 34.000
## nodefactor.deg.pers.2 -36.00 -12.000 0.0000 13.0000 37.000
## nodefactor.race..wa.B -22.00 -5.997 2.0032 11.0032 27.003
## nodefactor.race..wa.H -32.98 -9.978 1.0220 13.0220 36.022
## nodefactor.region.EW -35.56 -13.561 0.4392 12.4392 36.439
## nodefactor.region.OW -64.13 -22.131 -0.1306 21.8694 65.869
## nodematch.race..wa.B -11.18 -5.179 -2.1787 0.8213 7.821
## nodematch.race..wa.H -19.31 -8.312 -2.3124 3.6876 14.688
## nodematch.race..wa.O -46.89 -13.890 3.1103 21.1103 55.110
## absdiff.sqrt.age -54.97 -18.351 0.8125 20.0558 56.996
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.39803603
## nodefactor.deg.pers.1 0.3980360 1.00000000
## nodefactor.deg.pers.2 0.4265749 0.05266697
## nodefactor.race..wa.B 0.2754407 0.10618653
## nodefactor.race..wa.H 0.3431995 0.15255616
## nodefactor.region.EW 0.3117805 0.14904874
## nodefactor.region.OW 0.5795201 0.20767434
## nodematch.race..wa.B 0.1058512 0.03274386
## nodematch.race..wa.H 0.1435687 0.06196018
## nodematch.race..wa.O 0.8064535 0.31178496
## absdiff.sqrt.age 0.5408588 0.21681228
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42657491 0.275440744
## nodefactor.deg.pers.1 0.05266697 0.106186532
## nodefactor.deg.pers.2 1.00000000 0.103652379
## nodefactor.race..wa.B 0.10365238 1.000000000
## nodefactor.race..wa.H 0.14819383 -0.009688964
## nodefactor.region.EW 0.13584110 0.026104636
## nodefactor.region.OW 0.22963090 0.123645203
## nodematch.race..wa.B 0.03672171 0.554118408
## nodematch.race..wa.H 0.06453201 0.001400343
## nodematch.race..wa.O 0.34894917 -0.065514553
## absdiff.sqrt.age 0.22695322 0.155692041
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.3431994808 0.311780464
## nodefactor.deg.pers.1 0.1525561647 0.149048737
## nodefactor.deg.pers.2 0.1481938251 0.135841096
## nodefactor.race..wa.B -0.0096889639 0.026104636
## nodefactor.race..wa.H 1.0000000000 0.227869550
## nodefactor.region.EW 0.2278695500 1.000000000
## nodefactor.region.OW 0.1741556626 -0.004739117
## nodematch.race..wa.B 0.0007333622 0.008087375
## nodematch.race..wa.H 0.6342243801 0.127337395
## nodematch.race..wa.O -0.0800913160 0.221405949
## absdiff.sqrt.age 0.1874269960 0.163926801
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.579520124 0.1058512378
## nodefactor.deg.pers.1 0.207674337 0.0327438606
## nodefactor.deg.pers.2 0.229630900 0.0367217088
## nodefactor.race..wa.B 0.123645203 0.5541184078
## nodefactor.race..wa.H 0.174155663 0.0007333622
## nodefactor.region.EW -0.004739117 0.0080873751
## nodefactor.region.OW 1.000000000 0.0387071391
## nodematch.race..wa.B 0.038707139 1.0000000000
## nodematch.race..wa.H 0.069194620 0.0010921528
## nodematch.race..wa.O 0.492383179 0.0370213482
## absdiff.sqrt.age 0.311263695 0.0616358277
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.143568666 0.80645350
## nodefactor.deg.pers.1 0.061960177 0.31178496
## nodefactor.deg.pers.2 0.064532011 0.34894917
## nodefactor.race..wa.B 0.001400343 -0.06551455
## nodefactor.race..wa.H 0.634224380 -0.08009132
## nodefactor.region.EW 0.127337395 0.22140595
## nodefactor.region.OW 0.069194620 0.49238318
## nodematch.race..wa.B 0.001092153 0.03702135
## nodematch.race..wa.H 1.000000000 0.05951206
## nodematch.race..wa.O 0.059512063 1.00000000
## absdiff.sqrt.age 0.079368442 0.43312724
## absdiff.sqrt.age
## edges 0.54085879
## nodefactor.deg.pers.1 0.21681228
## nodefactor.deg.pers.2 0.22695322
## nodefactor.race..wa.B 0.15569204
## nodefactor.race..wa.H 0.18742700
## nodefactor.region.EW 0.16392680
## nodefactor.region.OW 0.31126369
## nodematch.race..wa.B 0.06163583
## nodematch.race..wa.H 0.07936844
## nodematch.race..wa.O 0.43312724
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.06769847 0.08450526 0.12427567
## Lag 2e+05 0.01913605 0.03598618 0.04909196
## Lag 3e+05 -0.01195674 0.01418958 0.04173636
## Lag 4e+05 0.02833462 0.02836868 0.01153597
## Lag 5e+05 -0.01753768 0.01964117 0.02594178
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.09004216 0.27349212 0.31137911
## Lag 2e+05 0.02041443 0.16821377 0.16190176
## Lag 3e+05 0.03033915 0.11929116 0.10847211
## Lag 4e+05 0.01434047 0.07685472 0.07813862
## Lag 5e+05 0.04063541 0.04487599 0.06572183
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.043676801 0.32841516 0.6243771
## Lag 2e+05 -0.011942795 0.19521696 0.4256352
## Lag 3e+05 0.005001991 0.11395529 0.3043325
## Lag 4e+05 -0.007661757 0.06358034 0.2137205
## Lag 5e+05 -0.005959610 0.03986989 0.1623219
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.07277617 0.045824270
## Lag 2e+05 0.01454248 0.006605204
## Lag 3e+05 0.02010015 0.018133145
## Lag 4e+05 0.03555090 0.053995987
## Lag 5e+05 -0.02389293 0.012927706
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.058127368 0.106784945 0.084638962
## Lag 2e+05 0.015981474 0.030917770 0.053413382
## Lag 3e+05 -0.002345449 0.007706333 -0.005218518
## Lag 4e+05 0.023762489 0.015328473 0.007115044
## Lag 5e+05 0.029154806 0.018762052 0.027315140
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.104696126 0.26781482 0.32775576
## Lag 2e+05 0.047993514 0.15354901 0.14110339
## Lag 3e+05 0.028429536 0.10833921 0.07518084
## Lag 4e+05 0.018589691 0.06829804 0.03246813
## Lag 5e+05 0.003874657 0.07363898 0.02601542
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000
## Lag 1e+05 0.054702581 0.350565021 0.5913932
## Lag 2e+05 -0.006931929 0.190634917 0.4006833
## Lag 3e+05 0.012351141 0.083244843 0.2963322
## Lag 4e+05 0.004772773 0.033128156 0.2476580
## Lag 5e+05 0.002691049 0.007670699 0.1835250
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.083256837 0.059988661
## Lag 2e+05 -0.001586909 0.045773387
## Lag 3e+05 -0.002775111 0.009448536
## Lag 4e+05 0.015176725 0.034558122
## Lag 5e+05 0.018336616 0.024023083
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.08264806 0.08678509 0.12362922
## Lag 2e+05 0.02418816 0.01216556 0.05112193
## Lag 3e+05 -0.01556547 0.01997431 0.02453039
## Lag 4e+05 -0.02173085 -0.01095952 -0.00673351
## Lag 5e+05 0.03597062 0.02978357 0.01268308
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.12733748 0.26787709 0.30310559
## Lag 2e+05 0.02496455 0.15303157 0.15061422
## Lag 3e+05 0.01385812 0.09061993 0.06231537
## Lag 4e+05 0.01768839 0.06833543 0.03994750
## Lag 5e+05 0.01608102 0.08663445 0.04287938
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.072291693 0.36793260 0.5954800
## Lag 2e+05 0.026190735 0.17626935 0.4128870
## Lag 3e+05 0.043426229 0.09710004 0.2913816
## Lag 4e+05 0.003300484 0.05027481 0.2150877
## Lag 5e+05 0.011305925 0.01389337 0.1724547
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.1038410622 0.067287476
## Lag 2e+05 0.0416023746 0.037905928
## Lag 3e+05 -0.0006556904 0.004060092
## Lag 4e+05 -0.0016237958 0.017588305
## Lag 5e+05 0.0187123507 0.007348827
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.082090345 0.0941312974 0.133765547
## Lag 2e+05 0.055422913 0.0273794031 0.058153450
## Lag 3e+05 0.027160862 0.0377146706 0.035485847
## Lag 4e+05 -0.003148203 -0.0003487154 0.005315735
## Lag 5e+05 0.019631761 -0.0297129099 0.009003562
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.147167292 0.28895239 0.31784171
## Lag 2e+05 0.048974458 0.16996550 0.15139281
## Lag 3e+05 0.011295667 0.12601999 0.10221487
## Lag 4e+05 0.041337600 0.10637520 0.08390794
## Lag 5e+05 0.008612259 0.05051538 0.06906387
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.064483707 0.33494875 0.6179727
## Lag 2e+05 0.029482801 0.16094630 0.4282253
## Lag 3e+05 0.021882323 0.09478520 0.3309401
## Lag 4e+05 -0.006627717 0.06752424 0.2696910
## Lag 5e+05 0.003625802 0.06924359 0.1980597
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.0000000000
## Lag 1e+05 0.09074607 0.0643487620
## Lag 2e+05 0.06067677 0.0511831876
## Lag 3e+05 0.02762856 0.0038282015
## Lag 4e+05 -0.03252625 -0.0009830458
## Lag 5e+05 0.01196113 0.0149702040
## Chain 5
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.00000000 1.00000000
## Lag 1e+05 0.0871839656 0.10640477 0.13304830
## Lag 2e+05 -0.0006012904 0.02460845 0.03242494
## Lag 3e+05 -0.0047012209 0.02806610 0.03697350
## Lag 4e+05 0.0407090025 0.01394590 0.01380634
## Lag 5e+05 0.0273277794 0.03759809 0.04606206
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.00000000 1.00000000
## Lag 1e+05 0.1144855523 0.28890091 0.33201168
## Lag 2e+05 0.0445736911 0.15591160 0.17544415
## Lag 3e+05 0.0156715456 0.08658692 0.13330484
## Lag 4e+05 0.0062961280 0.08084373 0.09830808
## Lag 5e+05 0.0007647875 0.07842940 0.05864423
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.07788923 0.35746436 0.6085691
## Lag 2e+05 -0.01504171 0.15225196 0.4096029
## Lag 3e+05 -0.02711553 0.08008288 0.2832178
## Lag 4e+05 -0.02479692 0.03924806 0.1963766
## Lag 5e+05 0.02502108 0.02714032 0.1403657
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.09467206 0.099903560
## Lag 2e+05 0.01517600 0.043591668
## Lag 3e+05 0.01277006 0.020914831
## Lag 4e+05 0.01863300 -0.004383458
## Lag 5e+05 0.02116707 0.006963833
## Chain 6
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.00000000 1.00000000
## Lag 1e+05 0.0632885938 0.08115076 0.09478295
## Lag 2e+05 0.0309815021 0.04876841 0.03836926
## Lag 3e+05 0.0009134427 0.01017291 0.03922493
## Lag 4e+05 0.0048518356 0.01203061 0.01192058
## Lag 5e+05 -0.0010088766 -0.03785248 0.01660604
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.11564811 0.29078848 0.33022509
## Lag 2e+05 0.05173619 0.17511071 0.13699029
## Lag 3e+05 0.01835578 0.12604080 0.05365017
## Lag 4e+05 0.02072823 0.10906178 0.03836945
## Lag 5e+05 -0.01062245 0.06892829 0.02449346
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.061240785 0.39054962 0.6105833
## Lag 2e+05 0.010127619 0.19843181 0.4358168
## Lag 3e+05 -0.012862872 0.11361069 0.3278611
## Lag 4e+05 0.001138956 0.05713833 0.2557085
## Lag 5e+05 -0.010358893 0.02982058 0.1992266
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.095956929 0.072165663
## Lag 2e+05 0.048676084 0.033918372
## Lag 3e+05 0.011609342 0.014302304
## Lag 4e+05 -0.004769609 0.008187703
## Lag 5e+05 0.003470246 -0.041222715
## Chain 7
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.073368698 0.09420235 0.09640380
## Lag 2e+05 0.020388014 0.03979274 0.05475085
## Lag 3e+05 0.013279007 0.03202856 0.02999588
## Lag 4e+05 0.003362564 0.02370104 0.01786371
## Lag 5e+05 0.027038761 0.02466338 0.01241350
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.11443950 0.29974279 0.32376437
## Lag 2e+05 0.05645318 0.18153577 0.13795350
## Lag 3e+05 0.03953093 0.12184209 0.08805129
## Lag 4e+05 0.01186373 0.09623942 0.06212355
## Lag 5e+05 0.01357648 0.08475468 0.04495137
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.062729937 0.35681429 0.6115427
## Lag 2e+05 -0.004569052 0.17422438 0.4326321
## Lag 3e+05 -0.019377796 0.09276404 0.3243880
## Lag 4e+05 0.003242795 0.05519628 0.2553474
## Lag 5e+05 0.014765830 0.01878335 0.2112783
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.103656485 0.034980517
## Lag 2e+05 0.036345485 0.003908467
## Lag 3e+05 0.009943704 0.043105316
## Lag 4e+05 0.006174267 0.021437626
## Lag 5e+05 0.032812194 0.015936807
## Chain 8
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.072984166 0.11499973 0.1100598561
## Lag 2e+05 0.038447790 0.04217309 0.0533309021
## Lag 3e+05 -0.003444058 0.03120280 0.0253988750
## Lag 4e+05 0.001604086 0.01526900 0.0007102311
## Lag 5e+05 -0.005691717 -0.01586836 0.0213135652
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.113798471 0.28070988 0.31376554
## Lag 2e+05 0.069099433 0.17884966 0.16641411
## Lag 3e+05 0.010606971 0.12177182 0.08600808
## Lag 4e+05 0.002251525 0.09331093 0.03416815
## Lag 5e+05 -0.017514241 0.09291253 0.02934792
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.00000000 1.0000000
## Lag 1e+05 0.0448147851 0.36730028 0.6164120
## Lag 2e+05 0.0342667595 0.19328599 0.4367704
## Lag 3e+05 0.0006232978 0.09626245 0.3460960
## Lag 4e+05 -0.0161024904 0.04838563 0.2839143
## Lag 5e+05 -0.0214297053 0.01094436 0.2197718
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.089061904 0.0601552905
## Lag 2e+05 0.019054614 0.0061859777
## Lag 3e+05 -0.022984289 -0.0003997992
## Lag 4e+05 -0.009096074 0.0227710582
## Lag 5e+05 0.011217787 0.0247159221
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.62341 -0.52021 0.09698
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -1.58954 -1.50051 -0.62173
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -1.03380 -1.42856 -2.16557
## nodematch.race..wa.O absdiff.sqrt.age
## -0.20740 1.73703
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.53301277 0.60291634 0.92274205
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.11193890 0.13348321 0.53411876
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.30122896 0.15313022 0.03034436
## nodematch.race..wa.O absdiff.sqrt.age
## 0.83570072 0.08238180
## Joint P-value (lower = worse): 0.5568118 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.05801 1.05709 0.09707
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 2.19196 0.02444 -0.37513
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 2.38079 1.57388 1.13458
## nodematch.race..wa.O absdiff.sqrt.age
## 1.18581 1.34409
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.29005143 0.29046840 0.92267224
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.02838208 0.98050031 0.70756107
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.01727542 0.11551430 0.25655246
## nodematch.race..wa.O absdiff.sqrt.age
## 0.23569591 0.17892031
## Joint P-value (lower = worse): 0.2930557 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.05403 0.18854 -0.98521
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.85673 -0.69373 0.79960
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.25782 1.63336 -0.43470
## nodematch.race..wa.O absdiff.sqrt.age
## 0.36144 0.34161
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.9569084 0.8504515 0.3245214
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.3915955 0.4878513 0.4239412
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.2084562 0.1023927 0.6637807
## nodematch.race..wa.O absdiff.sqrt.age
## 0.7177736 0.7326454
## Joint P-value (lower = worse): 0.6518567 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.008198 1.701945 1.145310
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.683424 0.395805 -0.626842
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.638576 0.918658 0.461561
## nodematch.race..wa.O absdiff.sqrt.age
## -0.524976 0.809372
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.99345936 0.08876567 0.25208087
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.09229313 0.69224893 0.53076312
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.52309855 0.35827460 0.64439619
## nodematch.race..wa.O absdiff.sqrt.age
## 0.59960021 0.41830132
## Joint P-value (lower = worse): 0.3607245 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.55876 0.21653 1.48989
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.66411 0.91962 0.06934
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.57068 0.06914 0.51709
## nodematch.race..wa.O absdiff.sqrt.age
## -0.19737 0.94259
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.5763279 0.8285739 0.1362540
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.5066224 0.3577713 0.9447173
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.5682133 0.9448763 0.6050915
## nodematch.race..wa.O absdiff.sqrt.age
## 0.8435404 0.3458913
## Joint P-value (lower = worse): 0.8826139 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.73170 2.06544 -0.78405
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -1.78296 -0.39481 -0.02227
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.39978 -2.39130 -0.38838
## nodematch.race..wa.O absdiff.sqrt.age
## 1.23568 1.02959
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.46435364 0.03888138 0.43301255
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.07459238 0.69298277 0.98222861
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.68931900 0.01678899 0.69773191
## nodematch.race..wa.O absdiff.sqrt.age
## 0.21657611 0.30320322
## Joint P-value (lower = worse): 0.1482674 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.58178 -0.33449 -0.57731
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.05409 1.53982 0.34516
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.46570 0.30696 2.36796
## nodematch.race..wa.O absdiff.sqrt.age
## -0.80842 -0.98066
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.56071766 0.73800858 0.56373086
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.95686340 0.12360376 0.72997074
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.64143232 0.75887312 0.01788669
## nodematch.race..wa.O absdiff.sqrt.age
## 0.41884832 0.32675988
## Joint P-value (lower = worse): 0.7430276 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.0789 1.4728 0.3105
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.8449 1.1911 0.9815
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.0658 -0.6463 0.5820
## nodematch.race..wa.O absdiff.sqrt.age
## -1.2526 0.5329
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.9371138 0.1408110 0.7562176
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.3981887 0.2335971 0.3263528
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.2865220 0.5180795 0.5605333
## nodematch.race..wa.O absdiff.sqrt.age
## 0.2103624 0.5941217
## Joint P-value (lower = worse): 0.2256065 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 8
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -2.283200 28.590 0.16507 0.17864
## nodefactor.deg.pers.1 -0.009533 17.471 0.10087 0.10981
## nodefactor.deg.pers.2 -0.433367 18.590 0.10733 0.11924
## nodefactor.race..wa.B 2.471667 12.485 0.07208 0.08189
## nodefactor.race..wa.H 2.035133 17.654 0.10193 0.15996
## nodefactor.region.EW -0.639000 17.859 0.10311 0.14533
## nodefactor.region.OW -0.513367 32.504 0.18766 0.19638
## nodematch.race..wa.B -1.948187 4.896 0.02827 0.04099
## nodematch.race..wa.H -2.149396 8.679 0.05011 0.10868
## nodematch.race..wa.O 2.239051 26.167 0.15107 0.16374
## absdiff.sqrt.age -0.261876 28.555 0.16486 0.17830
## nodematch.region -0.092667 29.148 0.16829 0.19223
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -57.52 -21.500 -2.5000 17.5000 53.500
## nodefactor.deg.pers.1 -34.00 -12.000 0.0000 12.0000 34.000
## nodefactor.deg.pers.2 -37.00 -13.000 -1.0000 12.0000 36.000
## nodefactor.race..wa.B -22.00 -5.997 2.0032 11.0032 27.003
## nodefactor.race..wa.H -32.98 -9.978 2.0220 14.0220 37.022
## nodefactor.region.EW -35.56 -12.561 -0.5608 11.4392 34.439
## nodefactor.region.OW -64.13 -22.131 -0.1306 21.8694 62.869
## nodematch.race..wa.B -11.18 -5.179 -2.1787 0.8213 7.821
## nodematch.race..wa.H -19.31 -8.312 -2.3124 3.6876 14.688
## nodematch.race..wa.O -49.89 -14.890 2.1103 20.1103 54.110
## absdiff.sqrt.age -56.45 -19.412 -0.5276 18.8196 56.718
## nodematch.region -56.45 -19.450 -0.4500 19.5500 56.550
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.39068785
## nodefactor.deg.pers.1 0.3906879 1.00000000
## nodefactor.deg.pers.2 0.4297938 0.04507891
## nodefactor.race..wa.B 0.2662599 0.10191372
## nodefactor.race..wa.H 0.3421319 0.14372709
## nodefactor.region.EW 0.3198379 0.13769265
## nodefactor.region.OW 0.5757142 0.20703655
## nodematch.race..wa.B 0.1105136 0.04093834
## nodematch.race..wa.H 0.1461889 0.06231053
## nodematch.race..wa.O 0.8039229 0.30960722
## absdiff.sqrt.age 0.5402218 0.22112245
## nodematch.region 0.8766448 0.33669935
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42979380 0.266259854
## nodefactor.deg.pers.1 0.04507891 0.101913722
## nodefactor.deg.pers.2 1.00000000 0.093997324
## nodefactor.race..wa.B 0.09399732 1.000000000
## nodefactor.race..wa.H 0.14180711 -0.008514599
## nodefactor.region.EW 0.14322363 0.034576420
## nodefactor.region.OW 0.23490603 0.123937652
## nodematch.race..wa.B 0.03546905 0.558842494
## nodematch.race..wa.H 0.06037293 0.002788320
## nodematch.race..wa.O 0.35574166 -0.074956029
## absdiff.sqrt.age 0.22776625 0.140927504
## nodematch.region 0.37699730 0.240554962
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.342131934 0.319837871
## nodefactor.deg.pers.1 0.143727094 0.137692647
## nodefactor.deg.pers.2 0.141807112 0.143223634
## nodefactor.race..wa.B -0.008514599 0.034576420
## nodefactor.race..wa.H 1.000000000 0.232531189
## nodefactor.region.EW 0.232531189 1.000000000
## nodefactor.region.OW 0.170814164 -0.002264736
## nodematch.race..wa.B -0.005692733 0.011413230
## nodematch.race..wa.H 0.636548087 0.125039482
## nodematch.race..wa.O -0.086732623 0.219689430
## absdiff.sqrt.age 0.179611092 0.176118833
## nodematch.region 0.291164315 0.231297690
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.575714209 0.110513570
## nodefactor.deg.pers.1 0.207036546 0.040938341
## nodefactor.deg.pers.2 0.234906027 0.035469049
## nodefactor.race..wa.B 0.123937652 0.558842494
## nodefactor.race..wa.H 0.170814164 -0.005692733
## nodefactor.region.EW -0.002264736 0.011413230
## nodefactor.region.OW 1.000000000 0.043685877
## nodematch.race..wa.B 0.043685877 1.000000000
## nodematch.race..wa.H 0.062799201 -0.009718621
## nodematch.race..wa.O 0.483664318 0.041850045
## absdiff.sqrt.age 0.311152200 0.050105838
## nodematch.region 0.492278387 0.104532405
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.146188867 0.80392288
## nodefactor.deg.pers.1 0.062310527 0.30960722
## nodefactor.deg.pers.2 0.060372928 0.35574166
## nodefactor.race..wa.B 0.002788320 -0.07495603
## nodefactor.race..wa.H 0.636548087 -0.08673262
## nodefactor.region.EW 0.125039482 0.21968943
## nodefactor.region.OW 0.062799201 0.48366432
## nodematch.race..wa.B -0.009718621 0.04185004
## nodematch.race..wa.H 1.000000000 0.05880039
## nodematch.race..wa.O 0.058800386 1.00000000
## absdiff.sqrt.age 0.078559012 0.43727180
## nodematch.region 0.124779162 0.70757297
## absdiff.sqrt.age nodematch.region
## edges 0.54022178 0.8766448
## nodefactor.deg.pers.1 0.22112245 0.3366994
## nodefactor.deg.pers.2 0.22776625 0.3769973
## nodefactor.race..wa.B 0.14092750 0.2405550
## nodefactor.race..wa.H 0.17961109 0.2911643
## nodefactor.region.EW 0.17611883 0.2312977
## nodefactor.region.OW 0.31115220 0.4922784
## nodematch.race..wa.B 0.05010584 0.1045324
## nodematch.race..wa.H 0.07855901 0.1247792
## nodematch.race..wa.O 0.43727180 0.7075730
## absdiff.sqrt.age 1.00000000 0.4731878
## nodematch.region 0.47318779 1.0000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.043734522 0.0452762862 0.067889708
## Lag 2e+05 0.009702439 0.0274039483 0.002161543
## Lag 3e+05 0.020811467 0.0358556822 0.010570927
## Lag 4e+05 0.011826248 0.0003215591 -0.001225525
## Lag 5e+05 0.032169974 -0.0047673324 0.009447099
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.10786853 0.20548078 0.25635531
## Lag 2e+05 0.05998099 0.10911683 0.10274977
## Lag 3e+05 0.03162528 0.10176333 0.06459846
## Lag 4e+05 0.02958939 0.06637427 0.03987217
## Lag 5e+05 0.02099115 0.04486566 0.02469043
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.045228938 0.32882151 0.4862013
## Lag 2e+05 0.025700076 0.16920267 0.3183952
## Lag 3e+05 -0.004237268 0.10489950 0.2256394
## Lag 4e+05 -0.009633616 0.06019234 0.1499446
## Lag 5e+05 0.034262557 0.04763393 0.1136707
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.062026242 0.04058624 0.07295450
## Lag 2e+05 0.017175290 0.02508536 0.03268258
## Lag 3e+05 0.013241663 0.03679810 0.03721407
## Lag 4e+05 0.003769886 0.03386080 0.03840731
## Lag 5e+05 0.003816299 0.03176452 0.01906072
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.055985620 0.066668778 0.093178224
## Lag 2e+05 0.019940993 0.001110535 0.027243369
## Lag 3e+05 0.002188858 0.011075252 0.021388839
## Lag 4e+05 -0.031013938 0.001654960 -0.039155065
## Lag 5e+05 0.003311521 0.001566750 -0.009455574
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.062295526 0.18909789 0.22747617
## Lag 2e+05 0.013236770 0.11817673 0.11495528
## Lag 3e+05 -0.022433609 0.07345865 0.07280288
## Lag 4e+05 -0.001079992 0.05581825 0.02803557
## Lag 5e+05 0.001177883 0.04285035 0.01901731
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000
## Lag 1e+05 0.0696093120 0.282090989 0.4996247
## Lag 2e+05 0.0242642265 0.116680212 0.3365303
## Lag 3e+05 -0.0158895617 0.047421341 0.2575139
## Lag 4e+05 -0.0294692349 0.013206352 0.1737947
## Lag 5e+05 0.0002739821 -0.003914393 0.1388402
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.036197951 0.02764664 0.105499387
## Lag 2e+05 0.032421119 0.01615064 0.028925211
## Lag 3e+05 0.037874279 0.01509322 0.021262537
## Lag 4e+05 -0.031565809 -0.01795900 -0.014236014
## Lag 5e+05 0.001607759 0.03637608 0.007904565
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.058228430 0.078827038 0.077863670
## Lag 2e+05 -0.008589690 0.050920060 0.011501851
## Lag 3e+05 0.002605912 0.005052671 -0.005577724
## Lag 4e+05 0.021665842 0.031666385 0.026293295
## Lag 5e+05 -0.002079748 0.011558214 0.012621064
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.11735662 0.19821986 0.228705632
## Lag 2e+05 0.06452024 0.09100213 0.108828299
## Lag 3e+05 0.01366363 0.07496830 0.057578355
## Lag 4e+05 0.02313498 0.08234920 0.005553808
## Lag 5e+05 -0.00858346 0.05740002 0.024352709
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.06001571 0.32324762 0.4915075
## Lag 2e+05 -0.01253109 0.15624339 0.3038971
## Lag 3e+05 -0.02974309 0.05555642 0.2239145
## Lag 4e+05 -0.02364530 0.03836376 0.1600399
## Lag 5e+05 -0.03069267 0.04043075 0.1205130
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0995370517 0.074071150 0.0833437372
## Lag 2e+05 0.0041057905 0.006608629 0.0037962329
## Lag 3e+05 0.0239513620 0.001243460 -0.0006679481
## Lag 4e+05 -0.0031378816 0.013898432 0.0238994471
## Lag 5e+05 -0.0004786696 0.042654505 0.0016025899
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.019432027 0.054110468 0.073255934
## Lag 2e+05 -0.014268955 0.009566204 -0.010831854
## Lag 3e+05 0.016314029 0.002208092 0.007331121
## Lag 4e+05 0.038120562 0.040485286 0.002762327
## Lag 5e+05 -0.006369983 -0.009766635 0.013134261
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.10020586 0.22459916 0.20408318
## Lag 2e+05 0.04083383 0.15712389 0.10181753
## Lag 3e+05 0.03675655 0.09084067 0.04160851
## Lag 4e+05 0.04183225 0.09603325 0.05578569
## Lag 5e+05 0.03023276 0.05372364 0.02968607
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.046344612 0.29486169 0.5329943
## Lag 2e+05 -0.005421391 0.13624992 0.3625291
## Lag 3e+05 0.001812990 0.08562288 0.2820334
## Lag 4e+05 0.007226342 0.07133442 0.2200660
## Lag 5e+05 0.006668920 0.05446152 0.1877993
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.044222569 0.016756290 0.044720479
## Lag 2e+05 0.003767060 0.007003463 -0.022845562
## Lag 3e+05 0.017920092 0.011367346 0.026650168
## Lag 4e+05 0.006493209 0.007387843 0.027685362
## Lag 5e+05 0.005626490 -0.028752545 0.005301128
## Chain 5
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.067526741 0.059014865 0.080213204
## Lag 2e+05 0.010527153 0.004609703 0.030242338
## Lag 3e+05 -0.003617346 0.001983233 0.007098984
## Lag 4e+05 -0.023568487 -0.026026479 0.007402689
## Lag 5e+05 -0.012587047 -0.022183237 -0.013968802
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.073494233 0.20886628 0.26145863
## Lag 2e+05 0.041917588 0.11845244 0.14165433
## Lag 3e+05 0.009160498 0.10859512 0.04800362
## Lag 4e+05 0.001321283 0.04450234 0.01334600
## Lag 5e+05 -0.019845657 0.05560386 0.03008237
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.000000000 1.0000000
## Lag 1e+05 0.02032692 0.283060406 0.4659494
## Lag 2e+05 0.01465912 0.128204738 0.2892391
## Lag 3e+05 0.01183201 0.033054459 0.2295557
## Lag 4e+05 -0.01409405 -0.001839996 0.1522515
## Lag 5e+05 0.01316502 -0.004191450 0.1089999
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.084242088 0.051827020 0.068822236
## Lag 2e+05 0.002382625 0.012809847 0.022593102
## Lag 3e+05 -0.014230502 0.002921882 0.007223549
## Lag 4e+05 -0.030499592 -0.012607878 0.003119688
## Lag 5e+05 -0.006722295 -0.010541916 -0.004346334
## Chain 6
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.000000e+00 1.000000000
## Lag 1e+05 0.07164093 6.971138e-02 0.056196213
## Lag 2e+05 0.01379582 2.247606e-02 0.029364851
## Lag 3e+05 -0.01000389 5.970932e-05 0.006765946
## Lag 4e+05 0.01535555 6.300349e-03 0.024063278
## Lag 5e+05 0.03401446 7.444677e-03 0.017040298
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.075909585 0.22689197 0.26411651
## Lag 2e+05 0.026524500 0.15073361 0.12398903
## Lag 3e+05 0.014091213 0.11453671 0.07201562
## Lag 4e+05 -0.006682786 0.08404663 0.04966221
## Lag 5e+05 -0.004805165 0.07923275 0.05040471
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000
## Lag 1e+05 0.035795562 0.275621821 0.5313388
## Lag 2e+05 0.002069138 0.095002933 0.3944062
## Lag 3e+05 -0.001277394 0.029932830 0.2865855
## Lag 4e+05 -0.011548711 0.023554784 0.2157031
## Lag 5e+05 0.018214502 0.002002011 0.1841413
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.076163295 0.045016696 0.09029961
## Lag 2e+05 0.008149333 -0.007047222 0.02642242
## Lag 3e+05 -0.003907466 -0.016118821 0.01078445
## Lag 4e+05 0.007971958 0.019446175 0.02840745
## Lag 5e+05 0.009098474 -0.015831898 0.02604994
## Chain 7
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.04324999 0.066631990 0.10540752
## Lag 2e+05 0.04215013 0.020049717 0.02698084
## Lag 3e+05 0.00159763 -0.003302934 0.03485740
## Lag 4e+05 0.01554202 -0.013668793 0.02142753
## Lag 5e+05 0.01858660 -0.003893079 0.00842313
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.116015031 0.21466827 0.18865678
## Lag 2e+05 0.053302518 0.14663227 0.07840543
## Lag 3e+05 0.017589327 0.09727127 0.04781706
## Lag 4e+05 -0.011044547 0.08737686 0.05557408
## Lag 5e+05 0.009518817 0.05679969 0.02743568
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000
## Lag 1e+05 0.071171855 0.336107866 0.5139173
## Lag 2e+05 0.017617578 0.136968271 0.3435826
## Lag 3e+05 0.003853949 0.073786385 0.2556272
## Lag 4e+05 0.009172574 0.028666946 0.1853880
## Lag 5e+05 0.018755882 0.006497589 0.1283876
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.05136208 0.061764996 0.078893234
## Lag 2e+05 0.03374283 -0.006013913 0.047393032
## Lag 3e+05 0.01112571 0.007743583 -0.003668781
## Lag 4e+05 0.01801647 -0.000435902 0.010495603
## Lag 5e+05 0.02241667 0.003929233 0.023428056
## Chain 8
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.05506833 0.078675102 0.105192387
## Lag 2e+05 0.02129012 0.004796527 0.059199461
## Lag 3e+05 0.01990780 -0.006348493 0.038403430
## Lag 4e+05 0.02094310 -0.014218825 0.003352746
## Lag 5e+05 0.01742436 0.025941427 0.007701987
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000e+00 1.00000000 1.00000000
## Lag 1e+05 6.014802e-02 0.18962305 0.23033144
## Lag 2e+05 2.301030e-02 0.12727099 0.12269369
## Lag 3e+05 7.694215e-04 0.07435599 0.05892154
## Lag 4e+05 4.321569e-03 0.04766478 0.04262414
## Lag 5e+05 -7.926816e-05 0.05549153 0.02248845
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.00000000 1.0000000
## Lag 1e+05 0.0704687168 0.29997511 0.4974240
## Lag 2e+05 0.0066669637 0.11163044 0.3324487
## Lag 3e+05 0.0071931438 0.05503369 0.2325164
## Lag 4e+05 0.0206193049 0.03108122 0.1651131
## Lag 5e+05 0.0007112943 0.03670977 0.1150501
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.078201434 0.045122009 0.06300044
## Lag 2e+05 0.030764193 0.006219144 0.03494591
## Lag 3e+05 0.033287234 0.038553474 0.03272952
## Lag 4e+05 0.029320375 0.023355929 0.02810092
## Lag 5e+05 -0.003767702 0.032968046 0.01698691
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.64123 -0.22659 0.07302
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -1.13096 -1.74440 0.52571
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 2.36640 -0.68706 -1.59760
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 2.78729 1.43856 1.20193
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.100749260 0.820741092 0.941789130
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.258073888 0.081090101 0.599086995
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.017961877 0.492046358 0.110130973
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.005315072 0.150274679 0.229389978
## Joint P-value (lower = worse): 0.1653016 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.63774 0.87000 1.91064
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.92089 2.85162 2.12278
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.17383 -0.96972 2.17471
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.07345 -0.69553 0.78069
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.101476583 0.384298252 0.056050705
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.357107157 0.004349664 0.033771898
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.861999905 0.332184239 0.029651520
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.941448961 0.486724925 0.434986712
## Joint P-value (lower = worse): 0.03242592 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.8516 -0.6710 -0.3776
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -1.3919 1.1259 0.1312
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.4248 -2.5128 0.7415
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -2.0959 -0.8712 -1.7582
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.06407880 0.50219475 0.70573282
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.16395838 0.26021179 0.89563425
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.67098274 0.01197626 0.45839640
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.03608788 0.38365405 0.07871383
## Joint P-value (lower = worse): 0.4526964 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.42796 -0.11949 -0.44679
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.17183 1.13353 0.28689
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.10875 -0.03061 1.03805
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.72028 -2.31439 -0.11110
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.66868126 0.90488998 0.65502724
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.86357316 0.25699174 0.77419415
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.91340139 0.97558138 0.29924477
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.47135097 0.02064655 0.91153501
## Joint P-value (lower = worse): 0.7937427 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.51822 0.43516 1.20599
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.10432 -0.17644 0.03668
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.05064 0.50959 0.01259
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.22112 0.43211 -0.97706
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.6043058 0.6634477 0.2278201
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.9169169 0.8599490 0.9707388
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.9596151 0.6103354 0.9899581
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.8249954 0.6656637 0.3285398
## Joint P-value (lower = worse): 0.897573 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.2635 0.8207 0.5189
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -2.6033 -0.2743 -1.2445
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.8345 -0.6667 -0.8389
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 1.0125 1.0664 -0.3904
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.792158380 0.411833312 0.603815968
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.009234146 0.783874334 0.213321598
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.404011720 0.504941747 0.401509634
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.311297173 0.286241265 0.696206577
## Joint P-value (lower = worse): 0.3183716 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.39289 0.39859 0.24643
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.49187 2.17442 2.02798
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.17518 1.02345 1.43567
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.09123 0.64308 1.92523
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.16365187 0.69019448 0.80534828
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.13573441 0.02967395 0.04256230
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.86094186 0.30609582 0.15109662
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.92730587 0.52017397 0.05420090
## Joint P-value (lower = worse): 0.5400757 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.2831 0.7598 0.8050
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.2230 0.3815 -0.1229
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 2.0450 0.8999 0.3956
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.4601 0.1513 -0.2105
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.77712856 0.44737349 0.42084400
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.82355499 0.70282631 0.90219700
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.04085674 0.36816333 0.69237202
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.64547159 0.87971508 0.83330805
## Joint P-value (lower = worse): 0.6083809 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.m.buildup.unbal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55a92f5c7dd8>
##
## Iterations: 163 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -8.74450 0.03428 0 <1e-04 ***
## deg2+ -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 2
summary(est.m.buildup.unbal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55a95202b1d8>
##
## Iterations: 170 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -8.73179 0.03876 0 < 1e-04 ***
## nodefactor.race..wa.B -0.44891 0.08847 0 < 1e-04 ***
## nodefactor.race..wa.H 0.18424 0.06462 0 0.00435 **
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 3
summary(est.m.buildup.unbal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55a96f0f0a08>
##
## Iterations: 157 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 46.75 NA NA NA
## nodefactor.race..wa.B -56.06 NA NA NA
## nodefactor.race..wa.H -55.71 NA NA NA
## nodematch.race..wa.B 57.54 NA NA NA
## nodematch.race..wa.H 57.65 NA NA NA
## nodematch.race..wa.O -55.43 NA NA NA
## deg2+ -Inf 0.00 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 4
summary(est.m.buildup.unbal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55a98c39a028>
##
## Iterations: 108 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 44.61121 5502.51143 100 0.9935
## nodefactor.deg.pers.1 -0.36054 0.06311 0 <1e-04 ***
## nodefactor.deg.pers.2 -0.10996 0.05894 0 0.0621 .
## nodefactor.race..wa.B -53.80399 5502.51143 100 0.9922
## nodefactor.race..wa.H -53.44810 5502.51143 100 0.9922
## nodematch.race..wa.B 55.28566 5502.51143 100 0.9920
## nodematch.race..wa.H 55.39054 5502.51143 100 0.9920
## nodematch.race..wa.O -53.17687 5502.51143 100 0.9923
## deg2+ -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 5
summary(est.m.buildup.unbal[[5]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55a9a97a21b8>
##
## Iterations: 127 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 79.89172 NA NA NA
## nodefactor.deg.pers.1 -0.36978 0.06327 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.11815 0.05938 0 0.04663 *
## nodefactor.race..wa.B -88.82159 NA NA NA
## nodefactor.race..wa.H -88.41971 NA NA NA
## nodefactor.region.EW -0.20881 0.06956 0 0.00268 **
## nodefactor.region.OW -0.39047 0.04525 0 < 1e-04 ***
## nodematch.race..wa.B 90.26454 NA NA NA
## nodematch.race..wa.H 90.36732 NA NA NA
## nodematch.race..wa.O -88.15427 NA NA NA
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 6
summary(est.m.buildup.unbal[[6]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55a9c6cdcac8>
##
## Iterations: 84 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 9.592e+00 2.211e+04 100 0.99965
## nodefactor.deg.pers.1 -3.688e-01 6.335e-02 0 < 1e-04 ***
## nodefactor.deg.pers.2 -1.145e-01 5.927e-02 0 0.05330 .
## nodefactor.race..wa.B -1.741e+01 2.211e+04 100 0.99937
## nodefactor.race..wa.H -1.699e+01 2.211e+04 100 0.99939
## nodefactor.region.EW -2.180e-01 7.035e-02 0 0.00195 **
## nodefactor.region.OW -3.949e-01 4.471e-02 0 < 1e-04 ***
## nodematch.race..wa.B 1.884e+01 2.211e+04 100 0.99932
## nodematch.race..wa.H 1.894e+01 2.211e+04 100 0.99932
## nodematch.race..wa.O -1.673e+01 2.211e+04 100 0.99940
## absdiff.sqrt.age -1.397e+00 4.174e-02 0 < 1e-04 ***
## deg2+ -Inf 0.000e+00 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.000e+00 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.000e+00 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 7
summary(est.m.buildup.unbal[[7]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2)) +
## offset(nodemix("region", base = c(1, 3, 6)))
## <environment: 0x55a9e42760e8>
##
## Iterations: 91 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 48.864888 NA NA NA
## nodefactor.deg.pers.1 -0.369889 NA NA NA
## nodefactor.deg.pers.2 -0.116519 NA NA NA
## nodefactor.race..wa.B -56.200380 NA NA NA
## nodefactor.race..wa.H -55.763224 NA NA NA
## nodefactor.region.EW 0.684947 NA NA NA
## nodefactor.region.OW -0.009108 NA NA NA
## nodematch.race..wa.B 57.562403 NA NA NA
## nodematch.race..wa.H 57.646202 NA NA NA
## nodematch.race..wa.O -55.512942 NA NA NA
## absdiff.sqrt.age -1.397720 NA NA NA
## deg2+ -Inf 0.000000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.000000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.000000 0 <1e-04 ***
## mix.region.EW.KC -Inf 0.000000 0 <1e-04 ***
## mix.region.EW.OW -Inf 0.000000 0 <1e-04 ***
## mix.region.KC.OW -Inf 0.000000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R mix.region.EW.KC mix.region.EW.OW mix.region.KC.OW
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 8
summary(est.m.buildup.unbal[[8]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + nodematch("region",
## diff = FALSE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55aa01946cd0>
##
## Iterations: 121 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 38.79144 NA NA NA
## nodefactor.deg.pers.1 -0.36957 0.06291 0 <1e-04 ***
## nodefactor.deg.pers.2 -0.11506 0.06027 0 0.0563 .
## nodefactor.race..wa.B -48.86702 NA NA NA
## nodefactor.race..wa.H -48.43273 NA NA NA
## nodefactor.region.EW 0.58155 0.06196 0 <1e-04 ***
## nodefactor.region.OW -0.04167 0.03891 0 0.2841
## nodematch.race..wa.B 50.24087 NA NA NA
## nodematch.race..wa.H 50.33170 NA NA NA
## nodematch.race..wa.O -48.18119 NA NA NA
## absdiff.sqrt.age -1.39776 0.04163 0 <1e-04 ***
## nodematch.region 2.67294 0.07186 0 <1e-04 ***
## deg2+ -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Network diagnostics
Model 1
(dx_main1 <- netdx(est.m.buildup.unbal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.5 2235.633 -0.002 30.021
## nodefactor.deg.pers.1 NA 563.764 NA 16.918
## nodefactor.deg.pers.2 NA 618.830 NA 18.011
## nodefactor.race..wa.B NA 271.643 NA 13.242
## nodefactor.race..wa.H NA 486.702 NA 17.660
## nodefactor.region.EW NA 448.175 NA 16.769
## nodefactor.region.OW NA 1471.322 NA 31.104
## nodematch.race..wa.B NA 8.947 NA 2.716
## nodematch.race..wa.H NA 27.073 NA 5.267
## nodematch.race..wa.O NA 1542.219 NA 26.557
## absdiff.sqrt.age NA 2546.397 NA 48.613
## nodematch.region NA 994.955 NA 25.909
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.736 -0.178 120.208
## Pct Edges Diss 0.007 0.007 0.002 0.002plot(dx_main1, type="formation")
plot(dx_main1, type="duration")
plot(dx_main1, type="dissolution")
Model 2
(dx_main2 <- netdx(est.m.buildup.unbal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2237.017 -0.002 30.959
## nodefactor.deg.pers.1 NA 564.620 NA 18.357
## nodefactor.deg.pers.2 NA 618.896 NA 18.957
## nodefactor.race..wa.B 207.997 207.897 0.000 10.492
## nodefactor.race..wa.H 534.978 534.640 -0.001 17.227
## nodefactor.region.EW NA 458.497 NA 15.644
## nodefactor.region.OW NA 1467.086 NA 31.545
## nodematch.race..wa.B NA 4.949 NA 2.026
## nodematch.race..wa.H NA 32.543 NA 5.398
## nodematch.race..wa.O NA 1556.691 NA 26.253
## absdiff.sqrt.age NA 2546.621 NA 54.374
## nodematch.region NA 988.700 NA 26.077
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.931 -0.177 120.375
## Pct Edges Diss 0.007 0.007 -0.001 0.002plot(dx_main2, type="formation")
plot(dx_main2, type="duration")
plot(dx_main2, type="dissolution")
Model 3
(dx_main3 <- netdx(est.m.buildup.unbal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2230.763 -0.004 27.557
## nodefactor.deg.pers.1 NA 565.561 NA 16.934
## nodefactor.deg.pers.2 NA 619.835 NA 16.832
## nodefactor.race..wa.B 207.997 208.568 0.003 12.809
## nodefactor.race..wa.H 534.978 535.601 0.001 18.723
## nodefactor.region.EW NA 462.354 NA 14.782
## nodefactor.region.OW NA 1461.143 NA 25.792
## nodematch.race..wa.B 31.179 28.255 -0.094 4.669
## nodematch.race..wa.H 123.312 118.135 -0.042 7.910
## nodematch.race..wa.O 1638.890 1632.984 -0.004 24.374
## absdiff.sqrt.age NA 2532.996 NA 43.555
## nodematch.region NA 982.365 NA 27.138
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.393 -0.180 120.285
## Pct Edges Diss 0.007 0.007 0.002 0.002plot(dx_main3, type="formation")
plot(dx_main3, type="duration")
plot(dx_main3, type="dissolution")
Model 4
(dx_main4 <- netdx(est.m.buildup.unbal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2234.234 -0.003 28.221
## nodefactor.deg.pers.1 474.000 469.529 -0.009 16.456
## nodefactor.deg.pers.2 605.000 605.287 0.000 18.661
## nodefactor.race..wa.B 207.997 210.285 0.011 13.110
## nodefactor.race..wa.H 534.978 533.440 -0.003 16.421
## nodefactor.region.EW NA 459.833 NA 14.904
## nodefactor.region.OW NA 1463.297 NA 29.730
## nodematch.race..wa.B 31.179 29.109 -0.066 4.856
## nodematch.race..wa.H 123.312 118.511 -0.039 7.845
## nodematch.race..wa.O 1638.890 1638.129 0.000 26.364
## absdiff.sqrt.age NA 2550.844 NA 51.261
## nodematch.region NA 985.919 NA 26.061
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.501 -0.180 120.059
## Pct Edges Diss 0.007 0.007 -0.001 0.002plot(dx_main4, type="formation")
plot(dx_main4, type="duration")
plot(dx_main4, type="dissolution")
Model 5
(dx_main5 <- netdx(est.m.buildup.unbal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2229.784 -0.005 29.429
## nodefactor.deg.pers.1 474.000 470.846 -0.007 19.375
## nodefactor.deg.pers.2 605.000 607.813 0.005 18.641
## nodefactor.race..wa.B 207.997 210.939 0.014 11.368
## nodefactor.race..wa.H 534.978 528.805 -0.012 17.213
## nodefactor.region.EW 445.561 444.798 -0.002 15.827
## nodefactor.region.OW 1278.131 1272.563 -0.004 29.251
## nodematch.race..wa.B 31.179 29.281 -0.061 4.946
## nodematch.race..wa.H 123.312 117.882 -0.044 9.276
## nodematch.race..wa.O 1638.890 1637.202 -0.001 26.676
## absdiff.sqrt.age NA 2547.884 NA 43.266
## nodematch.region NA 1052.901 NA 24.958
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.152 -0.182 119.799
## Pct Edges Diss 0.007 0.007 0.004 0.002plot(dx_main5, type="formation")
plot(dx_main5, type="duration")
plot(dx_main5, type="dissolution")
Model 6
(dx_main6 <- netdx(est.m.buildup.unbal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2224.575 -0.007 27.779
## nodefactor.deg.pers.1 474.000 471.736 -0.005 18.258
## nodefactor.deg.pers.2 605.000 601.782 -0.005 17.804
## nodefactor.race..wa.B 207.997 210.446 0.012 12.697
## nodefactor.race..wa.H 534.978 523.001 -0.022 16.782
## nodefactor.region.EW 445.561 441.148 -0.010 17.335
## nodefactor.region.OW 1278.131 1268.319 -0.008 29.627
## nodematch.race..wa.B 31.179 29.160 -0.065 4.314
## nodematch.race..wa.H 123.312 112.825 -0.085 8.279
## nodematch.race..wa.O 1638.890 1633.114 -0.004 26.013
## absdiff.sqrt.age 1206.285 1205.860 0.000 24.810
## nodematch.region NA 1047.087 NA 26.621
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.937 -0.177 120.155
## Pct Edges Diss 0.007 0.007 0.001 0.002plot(dx_main6, type="formation")
plot(dx_main6, type="duration")
plot(dx_main6, type="dissolution")
Model 7
(dx_main7 <- netdx(est.m.buildup.unbal[[7]], nsims = 10, nsteps = 1000, ncores = 4))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2204.729 -0.016 27.361
## nodefactor.deg.pers.1 474.000 474.778 0.002 18.320
## nodefactor.deg.pers.2 605.000 594.970 -0.017 17.240
## nodefactor.race..wa.B 207.997 205.131 -0.014 11.888
## nodefactor.race..wa.H 534.978 501.902 -0.062 18.880
## nodefactor.region.EW 445.561 407.173 -0.086 18.656
## nodefactor.region.OW 1278.131 1270.100 -0.006 29.894
## nodematch.race..wa.B 31.179 26.880 -0.138 4.535
## nodematch.race..wa.H 123.312 97.033 -0.213 8.697
## nodematch.race..wa.O 1638.890 1621.609 -0.011 25.518
## absdiff.sqrt.age 1206.285 1213.460 0.006 24.093
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
## mix.region.EW.KC NA 0.000 NA 0.000
## mix.region.EW.OW NA 0.000 NA 0.000
## mix.region.KC.OW NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.994 -0.177 120.834
## Pct Edges Diss 0.007 0.007 -0.003 0.002plot(dx_main7, type="formation")
plot(dx_main7, type="duration")
plot(dx_main7, type="dissolution")
Model 8
(dx_main8 <- netdx(est.m.buildup.unbal[[7]], nsims = 10, nsteps = 1000, ncores = 4))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2196.336 -0.020 31.584
## nodefactor.deg.pers.1 474.000 472.158 -0.004 18.137
## nodefactor.deg.pers.2 605.000 597.268 -0.013 17.768
## nodefactor.race..wa.B 207.997 205.676 -0.011 11.247
## nodefactor.race..wa.H 534.978 499.571 -0.066 19.671
## nodefactor.region.EW 445.561 408.182 -0.084 19.966
## nodefactor.region.OW 1278.131 1264.950 -0.010 35.971
## nodematch.race..wa.B 31.179 27.002 -0.134 4.583
## nodematch.race..wa.H 123.312 97.848 -0.207 9.864
## nodematch.race..wa.O 1638.890 1615.939 -0.014 25.912
## absdiff.sqrt.age 1206.285 1212.473 0.005 29.059
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
## mix.region.EW.KC NA 0.000 NA 0.000
## mix.region.EW.OW NA 0.000 NA 0.000
## mix.region.KC.OW NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 124.803 -0.184 118.955
## Pct Edges Diss 0.007 0.007 0.005 0.002plot(dx_main8, type="formation")
plot(dx_main8, type="duration")
plot(dx_main8, type="dissolution")